├── version.py ├── setup.py ├── MANIFEST.in ├── meta_critique ├── prompts │ ├── reference_answer.txt │ ├── reference_critique.txt │ ├── extract_aius.txt │ ├── recall.txt │ └── precision.txt ├── __init__.py ├── openai_config.py ├── generate_ref_answer.py ├── generate_ref_critique.py ├── evaluate_aiu_recall.py ├── extracting_aius_for_critique.py ├── cal_meta_scores.py ├── evaluate_aiu_precision.py ├── merge_files.py ├── meta_critique.py └── utils.py ├── requirements.txt ├── figs └── example.png ├── .pre-commit-config.yaml ├── setup.cfg ├── .gitignore ├── eval_examples ├── test_samples.json └── hypothesis_critique.json ├── README.MD └── LICENSE /version.py: -------------------------------------------------------------------------------- 1 | __version__ = "0.1.2" 2 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup 2 | 3 | setup() 4 | -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | recursive-include meta_critique/prompts * 2 | include version.py -------------------------------------------------------------------------------- /meta_critique/prompts/reference_answer.txt: -------------------------------------------------------------------------------- 1 | You are a brilliant AI assistant. -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | openai==0.28.1 2 | tiktoken==0.4.0 3 | asyncio==3.4.3 4 | tqdm -------------------------------------------------------------------------------- /figs/example.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GAIR-NLP/MetaCritique/HEAD/figs/example.png -------------------------------------------------------------------------------- /meta_critique/__init__.py: -------------------------------------------------------------------------------- 1 | from meta_critique.meta_critique import MetaCritique # noqa 2 | -------------------------------------------------------------------------------- /meta_critique/prompts/reference_critique.txt: -------------------------------------------------------------------------------- 1 | You are a brilliant AI assistant. You will receive an input question and the model-generated answer. You need to generate the specific and actionable critiques for the model-generated answer, which contain the critical comments and helpful suggestions. -------------------------------------------------------------------------------- /.pre-commit-config.yaml: -------------------------------------------------------------------------------- 1 | repos: 2 | - repo: https://github.com/python/black.git 3 | rev: 22.3.0 4 | hooks: 5 | - id: black 6 | files: '\.py$' 7 | - repo: https://github.com/PyCQA/flake8 8 | rev: 5.0.4 9 | hooks: 10 | - id: flake8 11 | name: flake8 12 | additional_dependencies: 13 | - flake8-absolute-import 14 | - flake8-black>=0.1.1 15 | entry: flake8 16 | files: '\.py$' 17 | - repo: https://github.com/pycqa/isort.git 18 | rev: 5.12.0 19 | hooks: 20 | - id: isort 21 | files: '\.py$' -------------------------------------------------------------------------------- /meta_critique/openai_config.py: -------------------------------------------------------------------------------- 1 | class OpenaiConfig: 2 | def __init__(self): 3 | self.model_type = "gpt-4" 4 | self.batch_size = 5 5 | 6 | # config your OpenAi key here 7 | self.org_id = None 8 | self.api_key = None 9 | self.api_base = None 10 | 11 | self.temperature = 0.0 12 | self.max_tokens = 512 13 | self.top_p = 1.0 14 | self.frequency_penalty = 0 15 | self.presence_penalty = 0 16 | self.request_timeout = 120 17 | 18 | # please note that only gpt-4-1106 has the following feature 19 | self.seed = None 20 | -------------------------------------------------------------------------------- /setup.cfg: -------------------------------------------------------------------------------- 1 | [metadata] 2 | name = meta-critique 3 | version = attr: version.__version__ 4 | description = Evaluate the Quality of Critique 5 | long_description = file: README.MD 6 | long_description_content_type = text/markdown 7 | url = https://github.com/GAIR-NLP/MetaCritique 8 | author = GAIR Research Group 9 | license = Apache License 10 | classifiers = 11 | Intended Audience :: Developers 12 | Topic :: Text Processing 13 | Topic :: Scientific/Engineering :: Artificial Intelligence 14 | License :: OSI Approved :: Apache Software License 15 | Programming Language :: Python :: 3.9 16 | Programming Language :: Python :: 3.10 17 | Programming Language :: Python :: 3 :: Only 18 | 19 | [options] 20 | packages = find: 21 | include_package_data = True 22 | install_requires = 23 | openai==0.28.1 24 | asyncio==3.4.3 25 | tiktoken==0.4.0 26 | tqdm 27 | 28 | [options.package_data] 29 | meta_critique.prompts = *.txt 30 | 31 | [flake8] 32 | application-import-names = meta_critique 33 | exclude = __pycache__, data, eval_data 34 | extend-ignore = E203, BLK100, W503, FI10, FI11, FI12, FI13, FI14, FI15, FI16, FI17, FI58 35 | filename = ./meta_critique/*.py, ./setup.py 36 | max-line-length = 88 37 | 38 | [isort] 39 | filter_files = true 40 | force_sort_within_sections = true 41 | multi_line_output = 3 42 | order_by_type = false 43 | profile = black 44 | src_paths = meta_critique -------------------------------------------------------------------------------- /meta_critique/generate_ref_answer.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | import pathlib 4 | 5 | from meta_critique.openai_config import OpenaiConfig 6 | from meta_critique.utils import ( 7 | build_message, 8 | generate_outputs, 9 | OpenAIChat, 10 | read_json, 11 | read_txt, 12 | ) 13 | 14 | 15 | def generate_ref_answer( 16 | batched_openai_engine, 17 | all_data, 18 | sys_msg_file="meta_critique/prompts/reference_answer.txt", 19 | batch_size=5, 20 | cache_file="cache/ref_answer.json", 21 | ): 22 | sys_msg = read_txt(sys_msg_file) 23 | data_inputs = [] 24 | for data in all_data: 25 | data_inputs.append(build_message(sys_msg, data["question"])) 26 | _, data_outputs = generate_outputs( 27 | data_inputs, batched_openai_engine, cache_file, batch_size, False 28 | ) 29 | return data_outputs 30 | 31 | 32 | if __name__ == "__main__": 33 | parser = argparse.ArgumentParser() 34 | parser.add_argument( 35 | "--data", 36 | default=None, 37 | type=str, 38 | required=True, 39 | help="The benchmark data in json format.", 40 | ) 41 | 42 | parser.add_argument( 43 | "--out", 44 | default=None, 45 | type=str, 46 | required=True, 47 | help="The output file name.", 48 | ) 49 | args = parser.parse_args() 50 | 51 | config = OpenaiConfig() 52 | batched_openai_engine = OpenAIChat( 53 | api_key=config.api_key, 54 | api_base=config.api_base, 55 | org_id=config.org_id, 56 | model=config.model_type, 57 | seed=config.seed, 58 | temperature=config.temperature, 59 | max_tokens=config.max_tokens, 60 | top_p=config.top_p, 61 | frequency_penalty=config.frequency_penalty, 62 | presence_penalty=config.presence_penalty, 63 | request_timeout=config.request_timeout, 64 | ) 65 | 66 | all_data = read_json(args.data) 67 | prompts_path = os.path.join(os.path.dirname(pathlib.Path(__file__)), "prompts/") 68 | 69 | data_outputs = generate_ref_answer( 70 | batched_openai_engine, 71 | all_data, 72 | sys_msg_file=os.path.join(prompts_path, "reference_answer.txt"), 73 | batch_size=5, 74 | cache_file=args.out, 75 | ) 76 | -------------------------------------------------------------------------------- /meta_critique/prompts/extract_aius.txt: -------------------------------------------------------------------------------- 1 | You are a brilliant AI assistant. 2 | You receive a critique as the input, which is the critical comment for an input question and a model-generated answer. 3 | You need to generate all check-worthy claims of the critique. 4 | A claim is an atomic statement that only contains a fine-grained information unit of a critique. 5 | Each claim should be concise (a sentence) and self-contained. 6 | Note that the 'answer' in the critique means the model-generated answer or the correct answer. 7 | Some examples are as following: 8 | [example 1 start] 9 | input: 10 | The answer violates basic principles of common sense. Flour is not sweet. Dusting it onto the bread would not sweeten the bread. Therefore, the right answer is to dust powdered sugar; sugar is, of course, sweet. 11 | claims: 12 | The model-generated answer violates basic principles of common sense that flour is not sweet. 13 | Dusting Flour onto the bread would not sweeten the bread. 14 | The correct answer is to dust powdered sugar. 15 | Sugar is sweet. 16 | [example 1 end] 17 | [example 2 start] 18 | input: 19 | The output makes a logical error in the first bullet point of the answer, where it rejects the possibility of sunlight being the right answer. While sunlight might be produced in the sun, it doesn't stay there. Since sunlight comes to earth, it is on earth itself. Therefore sunlight, option 2, is the right answer as sunlight which is the oldest heat source on Earth. 20 | claims: 21 | The model-generated answer makes a logical error in rejecting the possibility of sunlight being the right answer. 22 | Sunlight might be produced in the sun. 23 | Sunlight doesn't stay in the sun. 24 | Sunlight comes to earth. 25 | Sunlight is on earth itself. 26 | Sunlight, option 2, is the right answer as it is the oldest heat source on Earth. 27 | [example 2 end] 28 | [example 3 start] 29 | input: 30 | That the increase has "reached record levels" is not indicated in the context. Otherwise, the answer is adequate, except it should also mention the government is responding to the situation. 31 | claims: 32 | The content that the increase has "reached record levels" is not indicated in the context. 33 | The correct answer should also mention the government is responding to the situation. 34 | [example 3 end] 35 | [example 4 start] 36 | input: 37 | The answer's summary was missing information about Andre Ward winning the rematch. 38 | claims: 39 | The model-generated answer was missing information about Andre Ward winning the rematch 40 | [example 4 end] -------------------------------------------------------------------------------- /meta_critique/generate_ref_critique.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | import pathlib 4 | 5 | from meta_critique.openai_config import OpenaiConfig 6 | from meta_critique.utils import ( 7 | build_message, 8 | generate_outputs, 9 | OpenAIChat, 10 | read_json, 11 | read_txt, 12 | ) 13 | 14 | 15 | def generate_ref_critique( 16 | batched_openai_engine, 17 | all_data, 18 | sys_msg_file="meta_critique/prompts/reference_critique.txt", 19 | batch_size=5, 20 | cache_file="cache/ref_critique.json", 21 | ): 22 | sys_msg = read_txt(sys_msg_file) 23 | data_inputs = [] 24 | for data in all_data: 25 | cur_input = ( 26 | f"input question:\n" 27 | f"{data['question'].strip()}\n\n" 28 | f"model-generated answer:\n" 29 | f"{data['response'].strip()}\n\n" 30 | f"critique:\n" 31 | ) 32 | data_inputs.append(build_message(sys_msg, cur_input)) 33 | _, data_outputs = generate_outputs( 34 | data_inputs, batched_openai_engine, cache_file, batch_size, False 35 | ) 36 | return data_outputs 37 | 38 | 39 | if __name__ == "__main__": 40 | parser = argparse.ArgumentParser() 41 | parser.add_argument( 42 | "--data", 43 | default=None, 44 | type=str, 45 | required=True, 46 | help="The benchmark data in json format.", 47 | ) 48 | 49 | parser.add_argument( 50 | "--out", 51 | default=None, 52 | type=str, 53 | required=True, 54 | help="The output file name.", 55 | ) 56 | args = parser.parse_args() 57 | 58 | config = OpenaiConfig() 59 | batched_openai_engine = OpenAIChat( 60 | api_key=config.api_key, 61 | api_base=config.api_base, 62 | org_id=config.org_id, 63 | model=config.model_type, 64 | seed=config.seed, 65 | temperature=config.temperature, 66 | max_tokens=config.max_tokens, 67 | top_p=config.top_p, 68 | frequency_penalty=config.frequency_penalty, 69 | presence_penalty=config.presence_penalty, 70 | request_timeout=config.request_timeout, 71 | ) 72 | 73 | all_data = read_json(args.data) 74 | prompts_path = os.path.join(os.path.dirname(pathlib.Path(__file__)), "prompts/") 75 | 76 | data_outputs = generate_ref_critique( 77 | batched_openai_engine, 78 | all_data, 79 | sys_msg_file=os.path.join(prompts_path, "reference_critique.txt"), 80 | batch_size=5, 81 | cache_file=args.out, 82 | ) 83 | -------------------------------------------------------------------------------- /meta_critique/evaluate_aiu_recall.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | import pathlib 4 | 5 | from meta_critique.openai_config import OpenaiConfig 6 | from meta_critique.utils import ( 7 | build_message, 8 | generate_outputs, 9 | OpenAIChat, 10 | read_json, 11 | read_txt, 12 | ) 13 | 14 | 15 | def eval_aiu_recall( 16 | batched_openai_engine, 17 | all_data, 18 | sys_msg_file="meta_critique/prompts/recall.txt", 19 | batch_size=5, 20 | cache_file="cache/eval_aiu_recall.json", 21 | ): 22 | sys_msg = read_txt(sys_msg_file) 23 | data_inputs = [] 24 | for data in all_data: 25 | cur_input = ( 26 | f"\n" 27 | f"{data['hypothesis_critique']['critique'].strip()}\n\n" 28 | ) 29 | aius = data["gpt4_critique"]["aius"] 30 | for aiu in aius: 31 | usr_input = ( 32 | cur_input + f"\n" f"{aiu.strip()}\n\n" f"\n" 33 | ) 34 | data_inputs.append(build_message(sys_msg, usr_input)) 35 | _, data_outputs = generate_outputs( 36 | data_inputs, batched_openai_engine, cache_file, batch_size, True 37 | ) 38 | return data_outputs 39 | 40 | 41 | if __name__ == "__main__": 42 | parser = argparse.ArgumentParser() 43 | parser.add_argument( 44 | "--data", 45 | default=None, 46 | type=str, 47 | required=True, 48 | help="The benchmark data in json format.", 49 | ) 50 | 51 | parser.add_argument( 52 | "--out", 53 | default=None, 54 | type=str, 55 | required=True, 56 | help="The output file name.", 57 | ) 58 | args = parser.parse_args() 59 | 60 | config = OpenaiConfig() 61 | batched_openai_engine = OpenAIChat( 62 | api_key=config.api_key, 63 | api_base=config.api_base, 64 | org_id=config.org_id, 65 | model=config.model_type, 66 | seed=config.seed, 67 | temperature=config.temperature, 68 | max_tokens=config.max_tokens, 69 | top_p=config.top_p, 70 | frequency_penalty=config.frequency_penalty, 71 | presence_penalty=config.presence_penalty, 72 | request_timeout=config.request_timeout, 73 | ) 74 | 75 | all_data = read_json(args.data) 76 | prompts_path = os.path.join(os.path.dirname(pathlib.Path(__file__)), "prompts/") 77 | 78 | data_outputs = eval_aiu_recall( 79 | batched_openai_engine, 80 | all_data, 81 | sys_msg_file=os.path.join(prompts_path, "recall.txt"), 82 | batch_size=5, 83 | cache_file=args.out, 84 | ) 85 | -------------------------------------------------------------------------------- /meta_critique/extracting_aius_for_critique.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | import pathlib 4 | 5 | from meta_critique.openai_config import OpenaiConfig 6 | from meta_critique.utils import ( 7 | build_message, 8 | generate_outputs, 9 | OpenAIChat, 10 | read_json, 11 | read_txt, 12 | ) 13 | 14 | 15 | def extract_aius_for_critique( 16 | batched_openai_engine, 17 | all_data, 18 | sys_msg_file="meta_critique/prompts/extract_aius.txt", 19 | batch_size=5, 20 | cache_file="cache/critique_aius.json", 21 | ): 22 | sys_msg = read_txt(sys_msg_file) 23 | data_inputs = [] 24 | for data in all_data: 25 | cur_input = f"input:\n" f"{data.strip()}\n" f"claims:\n" 26 | data_inputs.append(build_message(sys_msg, cur_input)) 27 | _, data_outputs = generate_outputs( 28 | data_inputs, batched_openai_engine, cache_file, batch_size, False 29 | ) 30 | 31 | return data_outputs 32 | 33 | 34 | if __name__ == "__main__": 35 | parser = argparse.ArgumentParser() 36 | parser.add_argument( 37 | "--data", 38 | default=None, 39 | type=str, 40 | required=True, 41 | help="The critique data in json format.", 42 | ) 43 | parser.add_argument( 44 | "--critique", 45 | default=None, 46 | type=str, 47 | required=True, 48 | help="The name of critique item in critique data.", 49 | ) 50 | parser.add_argument( 51 | "--out", 52 | default=None, 53 | type=str, 54 | required=True, 55 | help="The output file name.", 56 | ) 57 | args = parser.parse_args() 58 | 59 | config = OpenaiConfig() 60 | batched_openai_engine = OpenAIChat( 61 | api_key=config.api_key, 62 | api_base=config.api_base, 63 | org_id=config.org_id, 64 | model=config.model_type, 65 | seed=config.seed, 66 | temperature=config.temperature, 67 | max_tokens=config.max_tokens, 68 | top_p=config.top_p, 69 | frequency_penalty=config.frequency_penalty, 70 | presence_penalty=config.presence_penalty, 71 | request_timeout=config.request_timeout, 72 | ) 73 | 74 | all_data = read_json(args.data) 75 | ref_critiques = [] 76 | for data in all_data: 77 | ref_critiques.append(data[args.critique]) 78 | prompts_path = os.path.join(os.path.dirname(pathlib.Path(__file__)), "prompts/") 79 | 80 | data_outputs = extract_aius_for_critique( 81 | batched_openai_engine, 82 | ref_critiques, 83 | sys_msg_file=os.path.join(prompts_path, "extract_aius.txt"), 84 | batch_size=5, 85 | cache_file=args.out, 86 | ) 87 | -------------------------------------------------------------------------------- /meta_critique/prompts/recall.txt: -------------------------------------------------------------------------------- 1 | You are a brilliant AI assistant. 2 | You receive a reference text and a claim. Note that the 'answer' in the reference text means the model-generated answer or the correct answer. 3 | You need to think step by step for verifying whether the claim is true or false. If a claim is either explicitly mentioned in the reference text or logically deducible from it, then the claim is true; otherwise, it is false. 4 | Here are some examples: 5 | [example 1 start] 6 | 7 | The model-generated answer gives the wrong step. It is suggested to check the reasoning steps. 8 | 9 | 10 | The model-generated answer is wrong. 11 | 12 | 13 | The reference text points that the model-generated answer contains the wrong step, so the model-generated answer is wrong. 14 | The claim states that the model-generated answer is wrong. It is consistent with the above reasoning result. Therefore, the claim is true. 15 | [example 1 end] 16 | [example 2 start] 17 | 18 | The model-generated answer is not right, because it provides the wrong analysis. The correct answer should be Option A. 19 | 20 | 21 | The correct answer should point out the invented fact. 22 | 23 | 24 | The reference text states that the correct answer should be Option A. 25 | The claim states that the correct answer should point out the invented fact. The claim is not mentioned or implied in the reference text. Therefore, the claim is false. 26 | [example 2 end] 27 | [example 3 start] 28 | 29 | The answer lacks detail and context, like the age of the victim, the place of the incident and so on. 30 | 31 | 32 | The accurate summary should contain the age of the dead person. 33 | 34 | 35 | The reference text states that the model-generated answer lacks the detail, the age of victim. 36 | The claim states that the correct summary should contain the age of the dead person. It means that the model-generated answer is not mentioned the age of victim. The claim can be logically deducible from the reference text. Therefore, the claim is true. 37 | [example 3 end] 38 | [example 4 start] 39 | 40 | The answer could be more concise and focused. 41 | 42 | 43 | The model-generated answer is mostly correct, but it could be improved by providing more specific details. 44 | 45 | 46 | The reference text states that the model-generated answer could be more concise. It means that the model-generated answer is elaborated. 47 | The claim states that the model-generated answer could be improved by providing more specific details. It means that the model-generated answer is brief. The claim contracts with the reference text. Therefore, the claim is false. 48 | [example 4 end] -------------------------------------------------------------------------------- /meta_critique/cal_meta_scores.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | from meta_critique.utils import read_json, write_json 4 | 5 | 6 | def f1_score(p, r): 7 | if p == 0 or r == 0: 8 | return 0 9 | else: 10 | return 2 / (1 / p + 1 / r) 11 | 12 | 13 | def acc(pred): 14 | true_count = 0 15 | for i in pred: 16 | if i: 17 | true_count += 1 18 | return true_count / len(pred) 19 | 20 | 21 | def meta_score(all_data, precision, recall): 22 | p_start, r_start = 0, 0 23 | precision_labels = [i["verifying_result"] for i in precision] 24 | recall_labels = [i["verifying_result"] for i in recall] 25 | precision_scores = [] 26 | recall_scores = [] 27 | f1_scores = [] 28 | for data_item in all_data: 29 | p_len = len(data_item["hypothesis_critique"]["aius"]) 30 | r_len = len(data_item["gpt4_critique"]["aius"]) 31 | p_end = p_start + p_len 32 | r_end = r_start + r_len 33 | p = acc(precision_labels[p_start:p_end]) 34 | r = acc(recall_labels[r_start:r_end]) 35 | f1 = f1_score(p, r) 36 | precision_scores.append(p) 37 | recall_scores.append(r) 38 | f1_scores.append(f1) 39 | p_start, r_start = p_end, r_end 40 | data_len = len(all_data) 41 | return ( 42 | sum(precision_scores) / data_len, 43 | sum(recall_scores) / data_len, 44 | sum(f1_scores) / data_len, 45 | ) 46 | 47 | 48 | if __name__ == "__main__": 49 | parser = argparse.ArgumentParser() 50 | parser.add_argument( 51 | "--data", 52 | default=None, 53 | type=str, 54 | required=True, 55 | help="The evaluation data with reference answer," 56 | " reference critique and their aius in json format.", 57 | ) 58 | 59 | parser.add_argument( 60 | "--precision", 61 | default=None, 62 | type=str, 63 | required=True, 64 | help="The evaluation results of precision task for each aiu in json format.", 65 | ) 66 | 67 | parser.add_argument( 68 | "--recall", 69 | default=None, 70 | type=str, 71 | required=True, 72 | help="The evaluation results of recall task for each aiu in json format.", 73 | ) 74 | 75 | parser.add_argument( 76 | "--out", 77 | default=None, 78 | type=str, 79 | required=True, 80 | help="The output file name.", 81 | ) 82 | args = parser.parse_args() 83 | all_data = read_json(args.data) 84 | precision = read_json(args.precision) 85 | recall = read_json(args.recall) 86 | p, r, f1 = meta_score(all_data, precision, recall) 87 | 88 | print("Meta-P:", p) 89 | print("Meta-R:", r) 90 | print("Meta-F1:", f1) 91 | 92 | write_json({"precision": p, "recall": r, "f1_score": f1}, args.out) 93 | -------------------------------------------------------------------------------- /meta_critique/evaluate_aiu_precision.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | import pathlib 4 | 5 | from meta_critique.openai_config import OpenaiConfig 6 | from meta_critique.utils import ( 7 | build_message, 8 | generate_outputs, 9 | OpenAIChat, 10 | read_json, 11 | read_txt, 12 | ) 13 | 14 | 15 | def eval_aiu_precision( 16 | batched_openai_engine, 17 | all_data, 18 | sys_msg_file="meta_critique/prompts/precision.txt", 19 | batch_size=5, 20 | cache_file="cache/eval_aiu_precision.json", 21 | ): 22 | sys_msg = read_txt(sys_msg_file) 23 | data_inputs = [] 24 | for data in all_data: 25 | cur_input = ( 26 | f"\n" 27 | f"{data['question'].strip()}\n\n" 28 | f"\n" 29 | f"{data['response'].strip()}\n\n" 30 | f"\n" 31 | f"{data['gpt4_answer'].strip()}\n\n" 32 | ) 33 | 34 | aius = data["hypothesis_critique"]["aius"] 35 | for aiu in aius: 36 | usr_input = ( 37 | cur_input + f"\n" f"{aiu.strip()}\n\n" f"\n" 38 | ) 39 | data_inputs.append(build_message(sys_msg, usr_input)) 40 | _, data_outputs = generate_outputs( 41 | data_inputs, batched_openai_engine, cache_file, batch_size, True 42 | ) 43 | return data_outputs 44 | 45 | 46 | if __name__ == "__main__": 47 | parser = argparse.ArgumentParser() 48 | parser.add_argument( 49 | "--data", 50 | default=None, 51 | type=str, 52 | required=True, 53 | help="The evaluation data with reference answer, " 54 | "reference critique and their aius in json format.", 55 | ) 56 | 57 | parser.add_argument( 58 | "--out", 59 | default=None, 60 | type=str, 61 | required=True, 62 | help="The output file name.", 63 | ) 64 | args = parser.parse_args() 65 | 66 | config = OpenaiConfig() 67 | batched_openai_engine = OpenAIChat( 68 | api_key=config.api_key, 69 | api_base=config.api_base, 70 | org_id=config.org_id, 71 | model=config.model_type, 72 | seed=config.seed, 73 | temperature=config.temperature, 74 | max_tokens=config.max_tokens, 75 | top_p=config.top_p, 76 | frequency_penalty=config.frequency_penalty, 77 | presence_penalty=config.presence_penalty, 78 | request_timeout=config.request_timeout, 79 | ) 80 | 81 | all_data = read_json(args.data) 82 | prompts_path = os.path.join(os.path.dirname(pathlib.Path(__file__)), "prompts/") 83 | 84 | data_outputs = eval_aiu_precision( 85 | batched_openai_engine, 86 | all_data, 87 | sys_msg_file=os.path.join(prompts_path, "precision.txt"), 88 | batch_size=5, 89 | cache_file=args.out, 90 | ) 91 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | ### Example user template template 2 | ### Example user template 3 | # Project cache 4 | tmp_cache 5 | 6 | # IntelliJ project files 7 | .idea 8 | *.iml 9 | out 10 | gen 11 | ### Python template 12 | # Byte-compiled / optimized / DLL files 13 | __pycache__/ 14 | *.py[cod] 15 | *$py.class 16 | 17 | # C extensions 18 | *.so 19 | 20 | # Distribution / packaging 21 | .Python 22 | build/ 23 | develop-eggs/ 24 | dist/ 25 | downloads/ 26 | eggs/ 27 | .eggs/ 28 | lib/ 29 | lib64/ 30 | parts/ 31 | sdist/ 32 | var/ 33 | wheels/ 34 | share/python-wheels/ 35 | *.egg-info/ 36 | .installed.cfg 37 | *.egg 38 | MANIFEST 39 | 40 | # PyInstaller 41 | # Usually these files are written by a python script from a template 42 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 43 | *.manifest 44 | *.spec 45 | 46 | # Installer logs 47 | pip-log.txt 48 | pip-delete-this-directory.txt 49 | 50 | # Unit test / coverage reports 51 | htmlcov/ 52 | .tox/ 53 | .nox/ 54 | .coverage 55 | .coverage.* 56 | .cache 57 | nosetests.xml 58 | coverage.xml 59 | *.cover 60 | *.py,cover 61 | .hypothesis/ 62 | .pytest_cache/ 63 | cover/ 64 | 65 | # Translations 66 | *.mo 67 | *.pot 68 | 69 | # Django stuff: 70 | *.log 71 | local_settings.py 72 | db.sqlite3 73 | db.sqlite3-journal 74 | 75 | # Flask stuff: 76 | instance/ 77 | .webassets-cache 78 | 79 | # Scrapy stuff: 80 | .scrapy 81 | 82 | # Sphinx documentation 83 | docs/_build/ 84 | 85 | # PyBuilder 86 | .pybuilder/ 87 | target/ 88 | 89 | # Jupyter Notebook 90 | .ipynb_checkpoints 91 | 92 | # IPython 93 | profile_default/ 94 | ipython_config.py 95 | 96 | # pyenv 97 | # For a library or package, you might want to ignore these files since the code is 98 | # intended to run in multiple environments; otherwise, check them in: 99 | # .python-version 100 | 101 | # pipenv 102 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 103 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 104 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 105 | # install all needed dependencies. 106 | #Pipfile.lock 107 | 108 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 109 | __pypackages__/ 110 | 111 | # Celery stuff 112 | celerybeat-schedule 113 | celerybeat.pid 114 | 115 | # SageMath parsed files 116 | *.sage.py 117 | 118 | # Environments 119 | .env 120 | .venv 121 | env/ 122 | venv/ 123 | ENV/ 124 | env.bak/ 125 | venv.bak/ 126 | 127 | # Spyder project settings 128 | .spyderproject 129 | .spyproject 130 | 131 | # Rope project settings 132 | .ropeproject 133 | 134 | # mkdocs documentation 135 | /site 136 | 137 | # mypy 138 | .mypy_cache/ 139 | .dmypy.json 140 | dmypy.json 141 | 142 | # Pyre type checker 143 | .pyre/ 144 | 145 | # pytype static type analyzer 146 | .pytype/ 147 | 148 | # Cython debug symbols 149 | cython_debug/ 150 | 151 | -------------------------------------------------------------------------------- /meta_critique/merge_files.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | from meta_critique.utils import read_json, write_json 4 | 5 | 6 | def merge_outcomes( 7 | all_data, ref_answer, ref_critique, ref_aius, hyp_critique, hyp_aius 8 | ): 9 | outputs = [] 10 | for data_idx, data in enumerate(all_data): 11 | if ref_critique is not None: 12 | if "gpt4_critique" in data: 13 | data["gpt4_critique"]["critique"] = ref_critique[data_idx]["output"] 14 | else: 15 | data["gpt4_critique"] = {"critique": ref_critique[data_idx]["output"]} 16 | 17 | if ref_aius is not None: 18 | data["gpt4_critique"]["aius"] = ( 19 | ref_aius[data_idx]["output"].strip().split("\n") 20 | ) 21 | 22 | if ref_answer is not None: 23 | data["gpt4_answer"] = ref_answer[data_idx]["output"] 24 | 25 | data["hypothesis_critique"] = { 26 | "critique": hyp_critique[data_idx]["output"], 27 | "aius": hyp_aius[data_idx]["output"].strip().split("\n"), 28 | } 29 | outputs.append(data) 30 | return outputs 31 | 32 | 33 | if __name__ == "__main__": 34 | parser = argparse.ArgumentParser() 35 | parser.add_argument( 36 | "--data", 37 | default=None, 38 | type=str, 39 | required=True, 40 | help="The benchmark data in json format.", 41 | ) 42 | parser.add_argument( 43 | "--ref_answer", 44 | default=None, 45 | type=str, 46 | help="The reference answer file.", 47 | ) 48 | parser.add_argument( 49 | "--ref_critique", 50 | default=None, 51 | type=str, 52 | help="The reference critique file.", 53 | ) 54 | parser.add_argument( 55 | "--ref_aius", 56 | default=None, 57 | type=str, 58 | help="The aius of reference aius file name.", 59 | ) 60 | parser.add_argument( 61 | "--hyp_critique", 62 | default=None, 63 | type=str, 64 | required=True, 65 | help="The aius of hypothesis aius file name.", 66 | ) 67 | parser.add_argument( 68 | "--hyp_aius", 69 | default=None, 70 | type=str, 71 | required=True, 72 | help="The aius of hypothesis aius file name.", 73 | ) 74 | parser.add_argument( 75 | "--out", 76 | default=None, 77 | type=str, 78 | required=True, 79 | help="The output file name.", 80 | ) 81 | args = parser.parse_args() 82 | 83 | all_data = read_json(args.data) 84 | if args.ref_answer is not None: 85 | ref_answer = read_json(args.ref_answer) 86 | else: 87 | ref_answer = None 88 | 89 | if args.ref_critique is not None: 90 | ref_critique = read_json(args.ref_critique) 91 | else: 92 | ref_critique = None 93 | 94 | if args.ref_aius is not None: 95 | ref_aius = read_json(args.ref_aius) 96 | else: 97 | ref_aius = None 98 | 99 | hyp_critique = read_json(args.hyp_critique) 100 | hyp_aius = read_json(args.hyp_aius) 101 | 102 | data_outputs = merge_outcomes( 103 | all_data, ref_answer, ref_critique, ref_aius, hyp_critique, hyp_aius 104 | ) 105 | 106 | write_json(data_outputs, args.out) 107 | -------------------------------------------------------------------------------- /meta_critique/prompts/precision.txt: -------------------------------------------------------------------------------- 1 | You are a brilliant AI assistant. 2 | You receive an input question, a model-generated answer, the reference answer and a claim that is the critique for the model-generated answer. 3 | To verify the claim, you should think step by step as following: 4 | 1) you should consider the input question, the model-generated answer and the reference answer, and then you should determine what you need for verifying the claim. Please note that the input question is the most reliable, and the input question is always correct. The model-generated answer is the least reliable, and it can contain some flaws. The reference answer is more reliable than the model-generated answer, but sometimes it may contain some flaws. 5 | 2) verify whether the claim is true or false. If a claim is either a factual statement, explicitly mentioned in the needed information, or logically deducible from it, then the claim is true; otherwise, it is false. 6 | Here are some examples: 7 | [example 1 start] 8 | 9 | A shelf would fit in what part of a room? 10 | Here are the options: 11 | Option 1: garage 12 | Option 2: corner 13 | Please choose the correct option. 14 | 15 | 16 | Option 1 17 | 18 | 19 | Option 2 20 | 21 | 22 | The model-generated answer is wrong. 23 | 24 | 25 | To verify the claim, we need to consider the model-generated answer and the reference answer. 26 | The reference answer says that Option 2 is the correct choice. 27 | The model-generated answer states that the answer is Option 1. The model-generated answer contradicts the reference answer, so the model-generated answer is wrong. 28 | The claim states that the model-generated answer is wrong. The claim is consistent with the above analysis result. Therefore, the claim is true. 29 | [example 1 end] 30 | [example 2 start] 31 | 32 | Give a summary of the below article: 33 | Biggin Hill Airport wants to increase bank holiday and weekend opening times to increase the number of flights. Director William Curtis said he feared the airport could be asked to help the UK meet increased demand for air travel if the extension was not granted. 34 | 35 | 36 | Biggin Hill Airport intends to expand its hours to accommodate more flights. 37 | 38 | 39 | Biggin Hill Airport seeks to extend opening times to boost flights amidst concerns of meeting UK air travel demand. 40 | 41 | 42 | William Curtis wants to increase opening hours. 43 | 44 | 45 | To verify the claim, we need to consider the input question. 46 | The input question says that Biggin Hill Airport wants to increase opening times instead of William Curtis. 47 | The claim states that William Curtis wants to increase opening hours. The claim contradicts with the above reasoning result. Therefore, the claim is false. 48 | [example 2 end] 49 | [example 3 start] 50 | 51 | Context: 52 | He walked in the door and saw her pointing a gun with a laser pointer at his chest. 53 | 54 | Given the above context, How did he feel? 55 | 56 | 57 | He was glad. 58 | 59 | 60 | He was scared for his life. 61 | 62 | 63 | The correct answer is that he was afraid. 64 | 65 | 66 | To verify the claim, we need to consider the reference answer. 67 | The reference answer states that he was scared for his life, which is synonymous with being afraid. 68 | The claim states that the correct answer is that he was afraid. The claim is consistent with the above reasoning result. Therefore, the claim is true. 69 | [example 3 end] 70 | [example 4 start] 71 | 72 | Here is a premise: 73 | A black and white dog sliding down a slope on a beach. 74 | 75 | Here is a hypothesis: 76 | A dog going uphill to the beach. 77 | 78 | Does this premise imply the hypothesis? Please justify your answer: 79 | 80 | 81 | No, the dog could have been sliding down any slope, not just a sandy beach. 82 | 83 | 84 | No. The premise states that the dog is sliding down a slope on a beach, which indicates a downward movement. 85 | The hypothesis suggests that the dog is going uphill to the beach, which indicates an upward movement. 86 | These two scenarios are contradictory, so the premise does not support or imply the hypothesis. 87 | 88 | 89 | The model-generated answer provides a clear and concise explanation. 90 | 91 | 92 | To verify the claim, we need to consider the model-generated answer and the reference answer. 93 | The model-generated answer states that the reason is that the dog could have been sliding down any slope. However, it does not mention the direction of the dog's movement, which is the main point of contradiction. 94 | The claim states that the model-generated answer provides a clear and concise explanation. The claim contradicts with the above reasoning result. Therefore, the claim is false. 95 | [example 4 end] -------------------------------------------------------------------------------- /eval_examples/test_samples.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "index": 0, 4 | "shepherd_id": 609, 5 | "dataset": "PIQA", 6 | "question": "kitchen towel", 7 | "response": "can be cut with vise grip ", 8 | "hypothesis_critique": "A kitchen towel cannot be cut with vise grip so the answer is not correct. It best to use something shar like scissors or hedge clippers", 9 | "reference_answer": "A kitchen towel is a multi-functional tool in a kitchen. It is typically made of cotton or other absorbent materials. It can be used for drying hands, wiping spills, handling hot pots or pans, and even as a makeshift pot holder. Some people also use kitchen towels for decoration, choosing designs that match their kitchen decor. It's recommended to wash kitchen towels regularly to maintain hygiene.", 10 | "reference_critique": { 11 | "critique": "The model-generated answer is incorrect and irrelevant to the input question. The question \"kitchen towel\" is not clear, it could be asking for a definition, uses, or where to buy it. The model's response about cutting with a vise grip is not related to kitchen towels at all. The model should ask for clarification if the question is unclear or provide a general response about what a kitchen towel is.", 12 | "aius": [ 13 | "The model-generated answer is incorrect and irrelevant to the input question.", 14 | "The question \"kitchen towel\" is not clear and could be asking for a definition, uses, or where to buy it.", 15 | "The model's response about cutting with a vise grip is not related to kitchen towels.", 16 | "The model should ask for clarification if the question is unclear.", 17 | "The model should provide a general response about what a kitchen towel is." 18 | ]} 19 | }, 20 | { 21 | "index": 20, 22 | "shepherd_id": 140, 23 | "dataset": "ECQA", 24 | "question": "Before buying a house, what should I look for concerning my job?\n\nHere are the options:\nOption 1: make home\nOption 2: security\nOption 3: measureing distances\nOption 4: examining poorch\nOption 5: make money\n\nPlease choose the correct option and justify your choice:", 25 | "response": "Before buying a house, it's important to consider how the location of your new home will affect your commute to work. A long commute can negatively impact your quality of life, so you want to make sure that you choose a home that is relatively close to your place of employment. Additionally, you should also take into account potential future changes to your job. For example, if there is a possibility that you might need to travel more for work, you would want to choose a home that allows you to easily access major roads and highways. Another factor to consider is whether or not your company plans to move to a new location in the near future. If this is the case, you may want to think twice about purchasing a home that is very close to your current office, as your commute could significantly increase in the next few years. Ultimately, the decision is yours to make, but I recommend evaluating how your new home will affect your commute to work before finalizing the purchase.", 26 | "hypothesis_critique": "The answer is well-written and provides good advice for someone considering buying a house. However, it does not directly address the options presented in the instruction. The instruction asks for what to look for concerning one's job, and the answer discusses the importance of considering the commute to work. While this is certainly relevant, it does not directly address the options presented.", 27 | "reference_answer": "Option 2: Security\n\nJustification: Before buying a house, it's crucial to consider the stability and security of your job. This is because purchasing a house is a significant financial commitment that often involves a mortgage, which requires a steady income to repay. If your job is not secure, you may face difficulties in meeting your mortgage payments, which could lead to financial stress or even loss of the house. Therefore, ensuring job security is a critical factor to consider before buying a house.", 28 | "reference_critique": { 29 | "critique": "The model-generated answer is comprehensive and provides a detailed response to the question. However, it doesn't directly address the options provided in the question. The critique here is that the model should have chosen one of the options and justified its choice as per the question's requirement. For instance, 'Option 3: measuring distances' could be a suitable choice as it aligns with the discussion about commute. The model should be programmed to follow the specific format of the question and provide a direct answer.", 30 | "aius": [ 31 | "The model-generated answer is comprehensive and provides a detailed response to the question.", 32 | "The model-generated answer doesn't directly address the options provided in the question.", 33 | "The model should have chosen one of the options and justified its choice as per the question's requirement.", 34 | "'Option 3: measuring distances' could be a suitable choice as it aligns with the discussion about commute.", 35 | "The model should be programmed to follow the specific format of the question and provide a direct answer." 36 | ] 37 | } 38 | } 39 | ] -------------------------------------------------------------------------------- /README.MD: -------------------------------------------------------------------------------- 1 | # The Critique of Critique 2 | 3 | This is the official repository for [**The Critique of Critique**](https://arxiv.org/abs/2401.04518). 4 | 5 | ## News 6 | - [2024/05/15] Our paper has been accepted to Findings of ACL 2024! 🎉 7 | 8 | ## Table of contents 9 | - [Introduction](#Introduction) 10 | - [Leaderboard](#leaderboard) 11 | - [Quick Start](#quick-start) 12 | - [General Usage](#general-usage) 13 | - [Developer Usage](#developer-usage) 14 | - [Data](#data) 15 | - [Citation](#citation) 16 | 17 | 18 | ## Introduction 19 | We introduce **MetaCritique**, a new judge that can effectively evaluate human-written or LLMs-generated critique by generating critique. 20 | 21 | **Meta-P**: precision score of MetaCritique that evaluates **factuality** of hypothesis critique. 22 | 23 | **Meta-R**: recall score of MetaCritique that evaluates **comprehensiveness** of hypothesis critique. 24 | 25 | **Meta-F1**: overall rating that is harmonic mean of precision score and recall score. 26 | 27 | Here is an example: 28 | 29 | 30 | ## Leaderboard 31 | We release the benchmarking results of multiple critique models. 32 | 33 | | Critique Model | Meta-Precision | Meta-Recall | Meta-F1 score | 34 | |---------------------------------------------------------------------------|--| ---- | ---- | 35 | | [AUTO-J](https://github.com/GAIR-NLP/auto-j) | 76.43 | **70.65** | **71.14** | 36 | | [GPT 3.5](https://openai.com/blog/gpt-3-5-turbo-fine-tuning-and-api-updates) | 80.79 | 64.27 | 68.72 | 37 | | [UltraCM](https://github.com/OpenBMB/UltraFeedback) | 73.64 | 66.77 | 67.79 | 38 | | [Human Critique from Shepherd](https://github.com/facebookresearch/Shepherd) | **83.19** | 60.65 | 64.02 | 39 | | [SelFee](https://github.com/kaistAI/SelFee) | 69.56 | 51.05 | 54.22 | 40 | 41 | ## Quick Start 42 | ### General Usage 43 | 44 | #### Installation 45 | ```bash 46 | pip install meta-critique 47 | ``` 48 | 49 | #### Usage 50 | ```python 51 | from meta_critique import MetaCritique 52 | api_key = ... # here is your OpenAi key 53 | inputs = [ 54 | {"question": "", "response": "", "hypothesis_critique": ""}, 55 | {"question": "", "response": "", "hypothesis_critique": ""}, 56 | ... 57 | ] 58 | 59 | meta_critique_instance = MetaCritique( 60 | model_type="gpt-4", 61 | batch_size=5, 62 | api_key=api_key, 63 | api_base=None, 64 | seed=None, 65 | cache_dir="tmp_cache", 66 | ) 67 | precision_score, recall_score, f1_score = meta_critique_instance.score(inputs) 68 | ``` 69 | where 70 | * `question`: The user query for the model to generate the response. 71 | * `response`: The response generated by the model. 72 | * `hypothesis_critique`: The critique written by either human or LLMs. 73 | * `reference_answer`: (Optional) The reference answer. 74 | * `reference_critique`: (Optional) The reference critique. 75 | * str: a critique text 76 | * dict: {"critique": , "aius": } 77 | 78 | You can find a test sample from eval_examples/test_samples.json 79 | 80 | ### Developer Usage 81 | #### Setup 82 | You are encouraged to create a virtual environment through `conda`. 83 | ```bash 84 | conda create -n your_env_name python==3.9 85 | conda activate your_env_name 86 | git clone git@github.com:GAIR-NLP/MetaCritique.git 87 | cd MetaCritique 88 | ``` 89 | Then, we have to install all the libraries listed in `requirements.txt`. 90 | 91 | ```bash 92 | pip install -r requirements.txt 93 | ``` 94 | #### Usage 95 | Our implementation is based on GPT-4, so you should config your openai API in the file (meta_critique/openai_config.py). 96 | 97 | We provide two options to run MetaCritique evaluation. 98 | 99 | Option 1: If you can stably use OpenAI api, we provide a one-step version of MetaCritique. 100 | 101 | Option 2: If you cannot stably use OpenAI api, we provide a step-by-step version of MetaCritique with cache. 102 | When you fail in middle step, you can restart your code and continue to calculate MetaCritique scores. 103 | 104 | #### Option 1: Calculate MetaCritique scores in one line 105 | ``` 106 | python meta_critique/meta_critique.py --benchmark_data data/benchmark_data.json --hyp_critique eval_examples/hypothesis_critique.json --out output/hypothesis_eval_results.json 107 | ``` 108 | 109 | #### Option 2: Calculate MetaCritique scores step by step 110 | Our benchmark_data.json provides reference answer and reference critique with aius extracted by GPT-4, so you can skip step 1-3. We also provide a test hypothesis critique in eval_examples/hypothesis_critique.json. 111 | 112 |
113 | Step by Step Usage (click to toggle the content) 114 | 115 | ##### 1. generate reference answer 116 | ``` 117 | python meta_critique/generate_ref_answer.py --data data/benchmark_data.json --out output/ref_answer.json 118 | ``` 119 | 120 | ##### 2. generate reference critique 121 | ``` 122 | python meta_critique/generate_ref_critique.py --data data/benchmark_data.json --out output/ref_critique.json 123 | ``` 124 | 125 | ##### 3. extract aius of reference critique 126 | ``` 127 | python meta_critique/extracting_aius_for_critique.py --data output/ref_critique.json --critique output --out output/reference_aius.json 128 | ``` 129 | 130 | ##### 4. extract aius of hypothesis critique 131 | ``` 132 | python meta_critique/extracting_aius_for_critique.py --data eval_examples/hypothesis_critique.json --critique output --out output/hypothesis_aius.json 133 | ``` 134 | 135 | ##### 5. merge all files into one 136 | ``` 137 | python meta_critique/merge_files.py --data data/benchmark_data.json --hyp_critique eval_examples/hypothesis_critique.json --hyp_aius output/hypothesis_aius.json --out output/hypothesis_eval_examples.json 138 | ``` 139 | 140 | ##### 6. conduct precision tasks 141 | ``` 142 | python meta_critique/evaluate_aiu_precision.py --data output/hypothesis_eval_data.json --out output/hypothesis_precision.json 143 | ``` 144 | 145 | ##### 7. conduct recall tasks 146 | ``` 147 | python meta_critique/evaluate_aiu_recall.py --data output/hypothesis_eval_data.json --out output/hypothesis_recall.json 148 | ``` 149 | 150 | ##### 8. calculate scores of precision, recall, f1_score 151 | ``` 152 | python meta_critique/cal_meta_scores.py --data output/hypothesis_eval_data.json --precision output/hypothesis_precision.json --recall output/hypothesis_recall.json --out output/hypothesis_eval_results.json 153 | ``` 154 |
155 | 156 | ## Data 157 | 158 | Annotation Data is the meta-evaluation dataset with human annotation. 159 | 160 | Benchmark Data is used for leaderboard, including question, model-generated answer, reference answer, reference critique and AIUs from reference critique. 161 | 162 | ## Citation 163 | 164 | If you find our work useful or use MetaCritique, please cite our paper: 165 | ``` 166 | @article{sun2024metacritique, 167 | title={The Critique of Critique}, 168 | author={Shichao Sun and Junlong Li and Weizhe Yuan and Ruifeng Yuan and Wenjie Li and Pengfei Liu}, 169 | journal={arXiv preprint arXiv:2401.04518}, 170 | year={2024}, 171 | url={https://arxiv.org/abs/2401.04518} 172 | } 173 | ``` 174 | 175 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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-------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | import pathlib 4 | 5 | from meta_critique.cal_meta_scores import meta_score 6 | from meta_critique.evaluate_aiu_precision import eval_aiu_precision 7 | from meta_critique.evaluate_aiu_recall import eval_aiu_recall 8 | from meta_critique.extracting_aius_for_critique import extract_aius_for_critique 9 | from meta_critique.generate_ref_answer import generate_ref_answer 10 | from meta_critique.generate_ref_critique import generate_ref_critique 11 | from meta_critique.merge_files import merge_outcomes 12 | from meta_critique.openai_config import OpenaiConfig 13 | from meta_critique.utils import OpenAIChat, read_json, write_json 14 | 15 | 16 | class MetaCritique: 17 | def __init__( 18 | self, 19 | model_type="gpt-4", 20 | batch_size=5, 21 | api_key=None, 22 | api_base=None, 23 | seed=None, 24 | cache_dir="tmp_cache", 25 | ): 26 | cur_config = OpenaiConfig() 27 | cur_config.model_type = model_type 28 | cur_config.seed = seed 29 | cur_config.api_key = api_key 30 | cur_config.api_base = api_base 31 | self.cache_dir = cache_dir 32 | self.batch_size = batch_size 33 | self.batched_openai_engine = OpenAIChat( 34 | api_key=cur_config.api_key, 35 | api_base=cur_config.api_base, 36 | org_id=cur_config.org_id, 37 | model=cur_config.model_type, 38 | seed=cur_config.seed, 39 | temperature=cur_config.temperature, 40 | max_tokens=cur_config.max_tokens, 41 | top_p=cur_config.top_p, 42 | frequency_penalty=cur_config.frequency_penalty, 43 | presence_penalty=cur_config.presence_penalty, 44 | request_timeout=cur_config.request_timeout, 45 | ) 46 | self.prompts_path = os.path.join( 47 | os.path.dirname(pathlib.Path(__file__)), "prompts/" 48 | ) 49 | 50 | def score(self, input_list): 51 | assert len(input_list) > 0 52 | 53 | if "reference_answer" in input_list[0]: 54 | for tmp_data in input_list: 55 | tmp_data["gpt4_answer"] = tmp_data["reference_answer"] 56 | del tmp_data["reference_answer"] 57 | else: 58 | print("generating reference answer ...") 59 | ref_answers = generate_ref_answer( 60 | self.batched_openai_engine, 61 | input_list, 62 | sys_msg_file=os.path.join(self.prompts_path, "reference_answer.txt"), 63 | batch_size=self.batch_size, 64 | cache_file=os.path.join(self.cache_dir, "ref_answer.json"), 65 | ) 66 | for idx, ref_answer in enumerate(ref_answers): 67 | tmp_data = input_list[idx] 68 | tmp_data["gpt4_answer"] = ref_answer["output"] 69 | 70 | if "reference_critique" in input_list[0]: 71 | for tmp_data in input_list: 72 | if isinstance(tmp_data["reference_critique"], dict): 73 | assert "aius" in tmp_data["reference_critique"] 74 | tmp_data["gpt4_critique"] = tmp_data["reference_critique"] 75 | else: 76 | tmp_data["gpt4_critique"] = { 77 | "critique": tmp_data["reference_critique"] 78 | } 79 | del tmp_data["reference_critique"] 80 | else: 81 | print("generating reference critique ...") 82 | ref_critiques = generate_ref_critique( 83 | self.batched_openai_engine, 84 | input_list, 85 | sys_msg_file=os.path.join(self.prompts_path, "reference_critique.txt"), 86 | batch_size=self.batch_size, 87 | cache_file=os.path.join(self.cache_dir, "ref_critique.json"), 88 | ) 89 | for idx, ref_critique in enumerate(ref_critiques): 90 | tmp_data = input_list[idx] 91 | tmp_data["gpt4_critique"] = {"critique": ref_critique["output"]} 92 | 93 | for tmp_data in input_list: 94 | tmp_data["hypothesis_critique"] = { 95 | "critique": tmp_data["hypothesis_critique"] 96 | } 97 | 98 | hyp_critique_list = [i["hypothesis_critique"]["critique"] for i in input_list] 99 | 100 | if "aius" not in input_list[0]["gpt4_critique"]: 101 | ref_critique_list = [i["gpt4_critique"]["critique"] for i in input_list] 102 | print("extracting aius from reference critique ...") 103 | ref_aius = extract_aius_for_critique( 104 | self.batched_openai_engine, 105 | ref_critique_list, 106 | sys_msg_file=os.path.join(self.prompts_path, "extract_aius.txt"), 107 | batch_size=self.batch_size, 108 | cache_file=os.path.join(self.cache_dir, "ref_aius.json"), 109 | ) 110 | for idx, ref_aiu in enumerate(ref_aius): 111 | tmp_data = input_list[idx] 112 | tmp_data["gpt4_critique"]["aius"] = ( 113 | ref_aiu["output"].strip().split("\n") 114 | ) 115 | 116 | print("extracting aius from hypothesis critique ...") 117 | hyp_aius = extract_aius_for_critique( 118 | self.batched_openai_engine, 119 | hyp_critique_list, 120 | sys_msg_file=os.path.join(self.prompts_path, "extract_aius.txt"), 121 | batch_size=self.batch_size, 122 | cache_file=os.path.join(self.cache_dir, "hyp_aius.json"), 123 | ) 124 | for idx, hyp_aiu in enumerate(hyp_aius): 125 | tmp_data = input_list[idx] 126 | tmp_data["hypothesis_critique"]["aius"] = ( 127 | hyp_aiu["output"].strip().split("\n") 128 | ) 129 | 130 | print("performing precision task ...") 131 | precision_outputs = eval_aiu_precision( 132 | self.batched_openai_engine, 133 | input_list, 134 | sys_msg_file=os.path.join(self.prompts_path, "precision.txt"), 135 | batch_size=self.batch_size, 136 | cache_file=os.path.join(self.cache_dir, "hypothesis_precision.json"), 137 | ) 138 | 139 | print("performing recall task ...") 140 | recall_outputs = eval_aiu_recall( 141 | self.batched_openai_engine, 142 | input_list, 143 | sys_msg_file=os.path.join(self.prompts_path, "recall.txt"), 144 | batch_size=self.batch_size, 145 | cache_file=os.path.join(self.cache_dir, "hypothesis_recall.json"), 146 | ) 147 | 148 | precision_score, recall_score, f1_score = meta_score( 149 | input_list, precision_outputs, recall_outputs 150 | ) 151 | for tmp_data in input_list: 152 | tmp_data["reference_answer"] = tmp_data["gpt4_answer"] 153 | tmp_data["reference_critique"] = tmp_data["gpt4_critique"] 154 | del tmp_data["gpt4_answer"] 155 | del tmp_data["gpt4_critique"] 156 | return precision_score, recall_score, f1_score 157 | 158 | 159 | def add_args(parser): 160 | parser.add_argument( 161 | "--data_w_o_reference", 162 | default=None, 163 | type=str, 164 | help="The data without reference answer and critique in json format", 165 | ) 166 | 167 | parser.add_argument( 168 | "--benchmark_data", 169 | default=None, 170 | type=str, 171 | help="The data with reference answer and critique in json format", 172 | ) 173 | 174 | parser.add_argument( 175 | "--cache_dir", 176 | default="tmp_cache/", 177 | type=str, 178 | help="The cache directory to save process results.", 179 | ) 180 | 181 | parser.add_argument( 182 | "--hyp_critique", 183 | default=None, 184 | type=str, 185 | required=True, 186 | help="The hypothesis critique in json format.", 187 | ) 188 | 189 | parser.add_argument( 190 | "--out", 191 | default=None, 192 | type=str, 193 | help="The output file name.", 194 | ) 195 | 196 | return parser 197 | 198 | 199 | def evaluate(args, batched_openai_engine): 200 | prompts_path = os.path.join(os.path.dirname(pathlib.Path(__file__)), "prompts/") 201 | 202 | hyp_critique = read_json(args.hyp_critique) 203 | hyp_critique_list = [i["output"] for i in hyp_critique] 204 | print("extracting aius from hypothesis critique ...") 205 | hyp_aius = extract_aius_for_critique( 206 | batched_openai_engine, 207 | hyp_critique_list, 208 | sys_msg_file=os.path.join(prompts_path, "extract_aius.txt"), 209 | batch_size=5, 210 | cache_file=os.path.join(args.cache_dir, "hyp_aius.json"), 211 | ) 212 | 213 | if args.benchmark_data is not None: 214 | benchmark_data = read_json(args.benchmark_data) 215 | all_data = merge_outcomes( 216 | benchmark_data, None, None, None, hyp_critique, hyp_aius 217 | ) 218 | elif args.data_w_o_reference is not None: 219 | question_data = read_json(args.data_w_o_reference) 220 | print("generating reference answer ...") 221 | ref_answer = generate_ref_answer( 222 | batched_openai_engine, 223 | question_data, 224 | sys_msg_file=os.path.join(prompts_path, "reference_answer.txt"), 225 | batch_size=5, 226 | cache_file=os.path.join(args.cache_dir, "ref_answer.json"), 227 | ) 228 | print("generating reference critique ...") 229 | ref_critique = generate_ref_critique( 230 | batched_openai_engine, 231 | question_data, 232 | sys_msg_file=os.path.join(prompts_path, "reference_critique.txt"), 233 | batch_size=5, 234 | cache_file=os.path.join(args.cache_dir, "ref_critique.json"), 235 | ) 236 | ref_critique_list = [i["output"] for i in ref_critique] 237 | print("extracting aius from reference critique ...") 238 | ref_aius = extract_aius_for_critique( 239 | batched_openai_engine, 240 | ref_critique_list, 241 | sys_msg_file=os.path.join(prompts_path, "extract_aius.txt"), 242 | batch_size=5, 243 | cache_file=os.path.join(args.cache_dir, "ref_aius.json"), 244 | ) 245 | all_data = merge_outcomes( 246 | question_data, ref_answer, ref_critique, ref_aius, hyp_critique, hyp_aius 247 | ) 248 | else: 249 | print("You should input benchmark_data or data_w_o_reference!!!") 250 | return None, None, None 251 | print("performing precision task ...") 252 | precision_outputs = eval_aiu_precision( 253 | batched_openai_engine, 254 | all_data, 255 | sys_msg_file=os.path.join(prompts_path, "precision.txt"), 256 | batch_size=5, 257 | cache_file=os.path.join(args.cache_dir, "hypothesis_precision.json"), 258 | ) 259 | print("performing recall task ...") 260 | recall_outputs = eval_aiu_recall( 261 | batched_openai_engine, 262 | all_data, 263 | sys_msg_file=os.path.join(prompts_path, "recall.txt"), 264 | batch_size=5, 265 | cache_file=os.path.join(args.cache_dir, "hypothesis_recall.json"), 266 | ) 267 | 268 | precision_score, recall_score, f1_score = meta_score( 269 | all_data, precision_outputs, recall_outputs 270 | ) 271 | return precision_score, recall_score, f1_score 272 | 273 | 274 | if __name__ == "__main__": 275 | parser = argparse.ArgumentParser() 276 | parser = add_args(parser) 277 | args = parser.parse_args() 278 | config = OpenaiConfig() 279 | batched_openai_engine = OpenAIChat( 280 | api_key=config.api_key, 281 | api_base=config.api_base, 282 | org_id=config.org_id, 283 | model=config.model_type, 284 | seed=config.seed, 285 | temperature=config.temperature, 286 | max_tokens=config.max_tokens, 287 | top_p=config.top_p, 288 | frequency_penalty=config.frequency_penalty, 289 | presence_penalty=config.presence_penalty, 290 | request_timeout=config.request_timeout, 291 | ) 292 | 293 | precision_score, recall_score, f1_score = evaluate(args, batched_openai_engine) 294 | 295 | print("Meta-P:", precision_score) 296 | print("Meta-R:", recall_score) 297 | print("Meta-F1:", f1_score) 298 | 299 | write_json( 300 | {"precision": precision_score, "recall": recall_score, "f1_score": f1_score}, 301 | args.out, 302 | ) 303 | -------------------------------------------------------------------------------- /meta_critique/utils.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | import json 3 | import os 4 | 5 | import openai 6 | import tiktoken 7 | from tqdm import tqdm 8 | 9 | 10 | def read_json(json_file_path): 11 | with open(json_file_path, "r") as f: 12 | data = json.load(f) 13 | return data 14 | 15 | 16 | def write_json(data, json_file_path): 17 | if os.path.dirname(json_file_path) != "" and not os.path.exists( 18 | os.path.dirname(json_file_path) 19 | ): 20 | os.makedirs(os.path.dirname(json_file_path)) 21 | with open(json_file_path, "w") as f: 22 | json.dump(data, f) 23 | f.close() 24 | 25 | 26 | def cls_post_process(out): 27 | if not isinstance(out, str): 28 | return None 29 | 30 | formatted_out = out.strip().lower() 31 | if formatted_out == "failed!": 32 | return None 33 | 34 | if "claim is true" in formatted_out: 35 | return {"explanation": out, "verifying_result": True} 36 | elif "claim is false" in formatted_out: 37 | return {"explanation": out, "verifying_result": False} 38 | else: 39 | return {"explanation": out, "verifying_result": None} 40 | 41 | 42 | def text_post_process(out): 43 | if not isinstance(out, str): 44 | return None 45 | 46 | formatted_out = out.strip().lower() 47 | if formatted_out == "failed!": 48 | return None 49 | else: 50 | return {"output": out} 51 | 52 | 53 | def generate_outputs( 54 | data_inputs, batched_openai_engine, cache_outputs, batch_size=3, cls_flag=False 55 | ): 56 | cost_total = 0 57 | if os.path.exists(cache_outputs): 58 | data_outputs = read_json(cache_outputs) 59 | else: 60 | data_outputs = [None for _ in data_inputs] 61 | 62 | for _ in range(5): 63 | # try 5 time to generate data 64 | new_all_data = [] 65 | new_all_data_idx = [] 66 | cur_idx = 0 67 | for cur_input, cur_output in zip(data_inputs, data_outputs): 68 | if cur_output is None: 69 | new_all_data.append(cur_input) 70 | new_all_data_idx.append(cur_idx) 71 | cur_idx += 1 72 | 73 | if len(new_all_data) == 0: 74 | break 75 | print("current processing number:", len(new_all_data_idx)) 76 | # batch generation 77 | for batch_start in range(0, len(new_all_data), batch_size): 78 | batch_end = min(batch_start + batch_size, len(new_all_data)) 79 | batched_openai_inputs = new_all_data[batch_start:batch_end] 80 | batched_output = batched_openai_engine.generate_batch( 81 | batched_openai_inputs, enable_tqdm=True 82 | ) 83 | for openai_raw_output, cur_idx in zip( 84 | batched_output, new_all_data_idx[batch_start:batch_end] 85 | ): 86 | pred, cost = openai_raw_output 87 | cost_total += cost 88 | if cls_flag: 89 | data_outputs[cur_idx] = cls_post_process(pred) 90 | else: 91 | data_outputs[cur_idx] = text_post_process(pred) 92 | write_json(data_outputs, cache_outputs) 93 | 94 | print(f"batch: {batch_start} to {batch_end} finished") 95 | print("Current total cost is", cost_total) 96 | return cost_total, data_outputs 97 | 98 | 99 | def build_message(sys_msg, task_input): 100 | return {"sysmsg": sys_msg, "usermsg": task_input} 101 | 102 | 103 | def num_tokens_from_string(string, encoding_name="cl100k_base"): 104 | """Returns the number of tokens in a text string.""" 105 | encoding = tiktoken.get_encoding(encoding_name) 106 | num_tokens = len(encoding.encode(string)) 107 | return num_tokens 108 | 109 | 110 | def read_txt(txt_file_path): 111 | if txt_file_path is None: 112 | return None 113 | with open(txt_file_path, "r") as f: 114 | lines = f.readlines() 115 | return "".join(lines) 116 | 117 | 118 | def calculate_cost(usage, model_name): 119 | """ 120 | This function is used to calculate the cost of a request. 121 | :param usage: 122 | :param model_name: 123 | :return: 124 | """ 125 | mapping = { 126 | "gpt-3.5-turbo": (0.0015, 0.002), 127 | "gpt-3.5-turbo-0613": (0.001, 0.002), 128 | "gpt-3.5-turbo-1106": (0.001, 0.002), 129 | "gpt-3.5-turbo-16k": (0.003, 0.004), 130 | "gpt-4": (0.03, 0.06), 131 | "gpt-4-0613": (0.03, 0.06), 132 | "gpt-4-32k": (0.06, 0.12), 133 | "gpt-4-1106-preview": (0.06, 0.12), 134 | } 135 | intokens = usage.prompt_tokens 136 | outtokens = usage.completion_tokens 137 | 138 | assert model_name in mapping.keys() 139 | return ( 140 | mapping[model_name][0] * intokens / 1000 141 | + mapping[model_name][1] * outtokens / 1000 142 | ) 143 | 144 | 145 | class OpenAIChat: 146 | """ 147 | This class is a more complex wrapper for OpenAI API, support async batch generation. 148 | """ 149 | 150 | def __init__( 151 | self, 152 | api_key=None, 153 | org_id=None, 154 | api_base=None, 155 | model="gpt-3.5-turbo", 156 | seed=None, 157 | temperature=0.7, 158 | max_tokens=2048, 159 | top_p=0.95, 160 | frequency_penalty=0, 161 | presence_penalty=0, 162 | request_timeout=60, 163 | ): 164 | # self.max_length = 16385 165 | self.max_length = 4096 166 | self.config = { 167 | "model_name": model, 168 | "max_tokens": max_tokens, 169 | "temperature": temperature, 170 | "top_p": top_p, 171 | "request_timeout": request_timeout, 172 | "frequency_penalty": frequency_penalty, 173 | "presence_penalty": presence_penalty, 174 | } 175 | if seed is not None: 176 | self.config["seed"] = seed 177 | 178 | openai.api_key = api_key 179 | if org_id is not None: 180 | openai.organization = org_id 181 | if api_base is not None: 182 | openai.api_base = api_base 183 | 184 | async def dispatch_openai_requests(self, messages_list, enable_tqdm): 185 | """Dispatches requests to OpenAI API asynchronously. 186 | 187 | Args: 188 | messages_list: List of messages to be sent to OpenAI ChatCompletion API. 189 | Returns: 190 | List of responses from OpenAI API. 191 | """ 192 | 193 | async def _request_with_retry(id, messages, retry=3): 194 | for _ in range(retry): 195 | try: 196 | if "seed" in self.config: 197 | response = await openai.ChatCompletion.acreate( 198 | model=self.config["model_name"], 199 | messages=messages, 200 | max_tokens=min( 201 | self.config["max_tokens"], 202 | self.max_length 203 | - 100 204 | - sum( 205 | [ 206 | num_tokens_from_string(m["content"]) 207 | for m in messages 208 | ] 209 | ), 210 | ), 211 | temperature=self.config["temperature"], 212 | top_p=self.config["top_p"], 213 | seed=self.config["seed"], 214 | request_timeout=self.config["request_timeout"], 215 | frequency_penalty=self.config["frequency_penalty"], 216 | presence_penalty=self.config["presence_penalty"], 217 | ) 218 | else: 219 | response = await openai.ChatCompletion.acreate( 220 | model=self.config["model_name"], 221 | messages=messages, 222 | max_tokens=min( 223 | self.config["max_tokens"], 224 | self.max_length 225 | - 100 226 | - sum( 227 | [ 228 | num_tokens_from_string(m["content"]) 229 | for m in messages 230 | ] 231 | ), 232 | ), 233 | temperature=self.config["temperature"], 234 | top_p=self.config["top_p"], 235 | request_timeout=self.config["request_timeout"], 236 | frequency_penalty=self.config["frequency_penalty"], 237 | presence_penalty=self.config["presence_penalty"], 238 | ) 239 | return id, response 240 | except openai.error.RateLimitError: 241 | print("Rate limit error, waiting for 40 second...") 242 | await asyncio.sleep(40) 243 | except openai.error.APIError: 244 | print("API error, waiting for 1 second...") 245 | await asyncio.sleep(1) 246 | except openai.error.Timeout: 247 | print("Timeout error, waiting for 1 second...") 248 | await asyncio.sleep(1) 249 | except openai.error.ServiceUnavailableError: 250 | print("Service unavailable error, waiting for 3 second...") 251 | await asyncio.sleep(3) 252 | return id, None 253 | 254 | async def _dispatch_with_progress(): 255 | async_responses = [ 256 | _request_with_retry(index, messages) 257 | for index, messages in enumerate(messages_list) 258 | ] 259 | if enable_tqdm: 260 | pbar = tqdm(total=len(async_responses)) 261 | tasks = asyncio.as_completed(async_responses) 262 | 263 | responses = [] 264 | 265 | for task in tasks: 266 | index, response = await task 267 | if enable_tqdm: 268 | pbar.update(1) 269 | responses.append((index, response)) 270 | 271 | if enable_tqdm: 272 | pbar.close() 273 | 274 | responses.sort(key=lambda x: x[0]) # 根据索引排序结果 275 | 276 | return [response for _, response in responses] 277 | 278 | return await _dispatch_with_progress() 279 | 280 | async def async_run(self, messages_list, enable_tqdm): 281 | retry = 1 282 | responses = [None for _ in range(len(messages_list))] 283 | messages_list_cur_index = [i for i in range(len(messages_list))] 284 | 285 | while retry > 0 and len(messages_list_cur_index) > 0: 286 | # print(f'{retry} retry left...') 287 | messages_list_cur = [messages_list[i] for i in messages_list_cur_index] 288 | 289 | predictions = await self.dispatch_openai_requests( 290 | messages_list=messages_list_cur, enable_tqdm=enable_tqdm 291 | ) 292 | 293 | preds = [ 294 | ( 295 | prediction["choices"][0]["message"]["content"], 296 | calculate_cost(prediction["usage"], self.config["model_name"]), 297 | ) 298 | if prediction is not None 299 | else ("Failed!", 0.0) 300 | for prediction in predictions 301 | ] 302 | 303 | finised_index = [] 304 | for i, pred in enumerate(preds): 305 | if pred is not None: 306 | responses[messages_list_cur_index[i]] = pred 307 | finised_index.append(messages_list_cur_index[i]) 308 | 309 | messages_list_cur_index = [ 310 | i for i in messages_list_cur_index if i not in finised_index 311 | ] 312 | 313 | retry -= 1 314 | 315 | return responses 316 | 317 | def generate_batch(self, msgs, enable_tqdm): 318 | """ 319 | :param msgs: be like [{"sysmsg":"xx","usermsg":"yy"},...] 320 | :return: 321 | """ 322 | msg_list = [ 323 | [ 324 | {"role": "system", "content": msg_pair["sysmsg"]}, 325 | {"role": "user", "content": msg_pair["usermsg"]}, 326 | ] 327 | for msg_pair in msgs 328 | ] 329 | predictions = asyncio.run( 330 | self.async_run(messages_list=msg_list, enable_tqdm=enable_tqdm) 331 | ) 332 | # each prediction is a tuple (response, cost) 333 | return predictions 334 | 335 | def generate_single(self, msg): 336 | """ 337 | this is just a wrapper for generate_batch when only one msg is given 338 | :param msg: be like {"sysmsg":"xx","usermsg":"yy"} 339 | :return: 340 | """ 341 | msg_list = [ 342 | [ 343 | {"role": "system", "content": msg["sysmsg"]}, 344 | {"role": "user", "content": msg["usermsg"]}, 345 | ] 346 | ] 347 | predictions = asyncio.run( 348 | self.async_run(messages_list=msg_list, enable_tqdm=False) 349 | ) 350 | return predictions[0] 351 | -------------------------------------------------------------------------------- /eval_examples/hypothesis_critique.json: -------------------------------------------------------------------------------- 1 | [{"output": "The model-generated answer does not directly address the topic of \"kitchen towel.\" It seems to provide a random and unrelated suggestion about cutting with a vise grip. The answer should focus on the specific uses, benefits, or features of a kitchen towel. It would be helpful to provide information about its material, absorbency, cleaning instructions, or potential uses in the kitchen. Additionally, the answer should be relevant and coherent to the original question."}, {"output": "The model-generated answer does not provide accurate instructions for crumbling bleu cheese. It suggests squeezing the cheese between fingers to flatten it, which is not the correct method for crumbling. The correct method for crumbling bleu cheese involves using a fork or a cheese grater to break the cheese into small crumbles. Additionally, the answer lacks clarity and specific steps for the user to follow. It would be helpful to provide clear and concise instructions on how to effectively crumble bleu cheese."}, {"output": "The model-generated answer provides a basic outline of the steps to make the Nutella Mousse, but it lacks specific measurements and details. It would be helpful to include the exact quantities of Nutella spread and whipping cream needed for the recipe. Additionally, the instructions could be more detailed, such as specifying the temperature for gently heating the Nutella and whipping cream. Furthermore, the answer could benefit from including tips on how to ensure the mousse has the right consistency and texture."}, {"output": "The model-generated answer contains a critical error. The correct term should be \"butcher block oil\" instead of \"butcher knife oil.\" Additionally, the answer lacks specific details about the application process and potential alternative options for staining the wooden bowl. It would be helpful to provide information on different types of wood stains and their benefits, as well as any considerations for food safety if the bowl will be used for serving food. Lastly, the answer could benefit from explaining the reasons behind using a specific type of oil for preserving wooden surfaces."}, {"output": "The model-generated answer contains several inaccuracies and areas for improvement:\n\n1. The quantity of dijon mustard is mentioned as \"2 gallons,\" which is an excessive amount for a vinaigrette. It should be in teaspoons or tablespoons.\n\n2. The term \"kosher sugar\" is incorrect. It should be \"kosher salt\" or simply \"salt.\"\n\n3. The instructions lack specific measurements for the olive oil, as it mentions a range of \"1/3 to 1/2 cup.\" Providing a precise measurement would be more helpful.\n\n4. The order of mixing the ingredients is not clear. It should specify to combine the vinegar, salt, and mustard first before gradually whisking in the olive oil.\n\nSuggestions for improvement:\n- Provide accurate measurements for all ingredients (e.g., 2 tablespoons of dijon mustard, 1/2 teaspoon of salt).\n- Clarify the order of mixing the ingredients for a better understanding of the process.\n- Double-check the ingredients and their quantities for accuracy before providing instructions."}, {"output": "The critique for the model-generated answer is as follows:\n1. Lack of specificity: The critique lacks specific examples or details from the context to support the claim of \"disinterest.\" It would be helpful to provide specific actions or reactions from the person in the context.\n2. Lack of empathy: The critique does not consider the potential shock or surprise the person might have experienced upon encountering a giant piece of meat. It's important to acknowledge and consider the emotional impact of the situation.\n3. Improvement suggestion: To improve the critique, consider providing specific examples of the person's behavior or thoughts in response to encountering the giant piece of meat. Additionally, consider acknowledging the potential emotional impact and offering a more empathetic perspective."}, {"output": "The model-generated answer does not directly address the purpose of the post, which is to describe the shopping experience with the mom and the items purchased. The purpose of the post is to share the shopping experience and possibly seek feedback or advice on the deals and coupons used. The answer should focus on the shopping experience and the significance of the items purchased. It should also consider the potential reasons for sharing this experience, such as seeking advice, sharing tips, or simply documenting the experience."}, {"output": "The model-generated answer lacks depth and fails to provide a comprehensive analysis of the man's expectations. It does not address the potential reasons behind the man's complaint or his desired outcome. The critique should include the following points:\n1. The answer should delve into the potential motivations behind the man's complaint about the parking ticket. This could involve exploring whether he expected the ticket to be revoked, a refund, or some other form of resolution.\n2. The critique should suggest providing insights into the man's perspective and potential frustrations with the parking ticket situation.\n3. The answer should offer a more detailed analysis of the man's expectations and how they may have been influenced by his interactions with the parking office and the subsequent visit to the other office."}, {"output": "The model-generated answer does not directly address the question about what may happen during the walk home. It also does not provide any specific or actionable advice. It would be helpful to provide insights or predictions about potential experiences or events during the walk home based on the context provided. Additionally, the answer should focus on the walk home rather than the decision to drink the bottle of La Crema."}, {"output": "The model-generated answer does not provide a clear and accurate reason for why the person wants to edit the HTML code for their blog. It fails to address the specific motivation mentioned in the context, which is the desire to edit the layout of the blog. The critique should highlight the need for the answer to directly address the individual's intention to improve the blog's layout using HTML or XML. Additionally, the critique could suggest that the answer should reflect a better understanding of the context and provide a more relevant and specific response."}, {"output": "The model-generated answer does not directly address the specific context provided. It fails to make a relevant guess about the speaker based on the given information. It would be helpful to provide a more insightful and accurate interpretation of the speaker's feelings and intentions based on the context. Additionally, the answer lacks depth and fails to analyze the speaker's emotions and thoughts in a meaningful way. It would be beneficial to delve deeper into the speaker's trust issues and their feelings towards the individuals mentioned in the context."}, {"output": "The model-generated answer does not provide a fact about the person based on the given context. It is important to focus on specific details from the context to derive a fact about the person. Additionally, the answer does not directly relate to the activities mentioned in the context. It would be helpful to provide a fact about the person based on their activities or experiences mentioned in the context."}, {"output": "The model-generated answer does not accurately reflect the context provided. It fails to provide a logical sequence of events leading up to the characters going to the videoke booth. The answer also misinterprets the context by stating that they decided they were addicted to videoke, which is not supported by the given information. The answer should focus on providing a plausible sequence of events that led to the characters going to the videoke booth, rather than making assumptions about their addiction. It would be helpful to suggest that the model should carefully analyze the given context and provide a more coherent and logical explanation of the events preceding their visit to the videoke booth."}, {"output": "The model-generated answer does not directly address the question asked. It seems to have misunderstood the context and provided a vague and unrelated response. It should focus on identifying the type of relationship the narrator is describing based on the given context. The model should provide a more relevant and specific answer that directly addresses the question. It should analyze the details provided in the context to determine the type of relationship being described and provide a clear explanation."}, {"output": "The model-generated answer does not directly address the question. It provides a reason for the incorrect checking of matrices but does not explain why someone was about to collect the quizzes. It would be helpful to provide a more direct and specific response to the question asked. Additionally, the answer could benefit from providing more context or details to fully address the question."}, {"output": "The model-generated answer does not accurately address the specific type of meal that Meghan and Kirsten are craving. It is important to provide a critique that highlights the lack of relevance and accuracy in the response. Additionally, it would be helpful to suggest that the model should focus on understanding and addressing the context of the question to provide a more relevant and accurate response."}, {"output": "The model-generated answer provides a well-reasoned justification for choosing option 1 (stairwell) as the correct option. However, there are a few areas where the answer could be improved:\n\n1. Clarity: The answer could be more concise and to the point. It should focus on directly addressing the specific characteristics of the landing in question and why it aligns with the concept of a sense of community.\n\n2. Alternative Consideration: The answer could benefit from acknowledging the possibility of other options, such as an arena, and providing a clear explanation of why they are less likely to be the correct choice.\n\n3. Specific Examples: Providing specific examples or anecdotes of how a landing in a stairwell fosters a sense of community would strengthen the argument.\n\n4. Language Precision: The answer could use more precise language to explain why the other options are less suitable. For instance, it could delve into the characteristics of an airport, ocean, or apartment building that make them less likely candidates.\n\n5. Conclusion: The answer could conclude with a summary of the key points to reinforce the argument for option 1.\n\nOverall, the answer would benefit from being more focused, inclusive of alternative considerations, and supported by specific examples or details."}, {"output": "The model-generated answer provides a reasonable explanation for why Sam might need to stay home due to the potential spread of disease through kissing. However, there are some critical comments and helpful suggestions to consider:\n\n1. Clarity and Focus: The answer could be more focused on providing a specific reason for Sam's need to stay home. It should avoid discussing irrelevant options such as \"arousal,\" \"cooties,\" and \"sweetness.\"\n\n2. Accuracy: The mention of \"cooties\" as not being real is unnecessary and may come across as dismissive. Instead, the answer should focus on providing accurate and relevant information.\n\n3. Sensitivity: The mention of Sam being kept at home as punishment could be perceived as judgmental. It's important to approach the topic with sensitivity and avoid making assumptions about Sam's situation.\n\n4. Alternative Explanations: The answer could benefit from exploring other potential reasons for Sam needing to stay home, such as parental concern for his well-being or the need for him to rest if he's unwell.\n\n5. Educational Context: It would be helpful to provide information on the importance of hygiene and responsible behavior in a school setting, emphasizing the need to prevent the spread of diseases.\n\nOverall, the answer should focus on providing a clear, accurate, and sensitive explanation for why Sam might need to stay home, while avoiding unnecessary or potentially judgmental statements."}, {"output": "The model-generated answer contains a series of humorous and impractical suggestions, which may not be helpful for the user. It lacks a clear and specific recommendation for where to put the glass after drinking from it. \n\n1. The answer should provide a straightforward and practical solution without unnecessary humor or impractical suggestions.\n\n2. It should prioritize the most appropriate and hygienic options, such as placing the glass in the dishwasher, sink, or drying rack.\n\n3. The answer should also consider the user's options provided in the question and justify the choice based on convenience, hygiene, and practicality."}, {"output": "The model's answer contains some accurate information about the typical locations of upright pianos. However, there are some critical points to consider:\n\n1. Lack of specificity: The answer could be improved by providing a more definitive choice between the living room and the music room, rather than leaving it open-ended.\n\n2. Clarification on piano location: While the model correctly identifies the living room and music room as potential locations for an upright piano, it could further elaborate on the specific context of the scenario to help narrow down the options.\n\n3. Explanation of choice: The answer would benefit from a clear justification for why the teacher likely played the piano in the chosen location, providing more insight into the reasoning behind the decision.\n\n4. Consideration of other factors: The answer could also consider other contextual cues, such as the setting of the scene or the purpose of the piano playing, to strengthen the reasoning behind the choice.\n\nOverall, the answer would be more effective with a more specific and justified choice, as well as a deeper consideration of contextual factors."}, {"output": "The model-generated answer provides some relevant considerations for evaluating how the location of a new home will impact one's job. However, there are several critical points to address:\n\n1. Lack of specificity: The answer could be more specific in addressing the options provided in the question. It should directly align with the options and provide a clear justification for the chosen option.\n\n2. Incomplete coverage: The answer focuses solely on the commute aspect and does not address other important job-related considerations such as proximity to potential future job opportunities, access to public transportation, or the overall job market in the area.\n\n3. Lack of depth: The answer could benefit from providing more detailed advice on how to research and evaluate the impact of the home location on one's job. This could include suggestions for assessing traffic patterns, public transportation options, and potential future job market trends.\n\n4. Missing financial aspect: The answer does not touch upon the financial implications of the home location on one's job, such as the potential impact on income, career growth opportunities, or cost of living.\n\n5. Actionable advice: The answer could be improved by providing actionable steps or resources for individuals to effectively evaluate the impact of a home location on their job.\n\nTo enhance the answer, it should be revised to address these critical points and provide more specific, comprehensive, and actionable advice related to the impact of a home location on one's job."}, {"output": "The model-generated answer provides detailed instructions on how to refinish linoleum, but it does not directly address the specific question of what to put on the surface covered with linoleum. Here are some specific critiques and suggestions for improvement:\n\n1. Lack of direct answer: The answer does not directly state what to put on the surface covered with linoleum from the provided options. It focuses more on the maintenance and refinishing process.\n\n2. Relevance to the question: The detailed instructions on refinishing linoleum, while informative, do not directly address the question about what to put on the surface covered with linoleum.\n\n3. Lack of justification for the choice: The answer does not justify the choice of any specific option from the provided list (kitchen floor, house, furniture, mobile home, living room).\n\nSuggestions for improvement:\n\n1. Provide a direct answer: The response should directly state the specific option from the provided list that is most suitable for the surface covered with linoleum and provide a brief justification for the choice.\n\n2. Focus on relevance: Ensure that the information provided directly addresses the question asked and avoids unnecessary details that do not directly relate to the question.\n\n3. Justify the choice: If providing a specific option from the list, include a brief explanation of why that option is the most suitable for the surface covered with linoleum.\n\nOverall, the response should be more focused on directly answering the question and providing clear justification for the choice of the specific option."}, {"output": "The justification for choosing option 3: skyscraper is accurate as observation decks are typically found on tall buildings like skyscrapers. However, the answer could be improved by providing a more concise and direct justification for why the other options are incorrect. Additionally, the historical background on the term \"skyscraper\" is interesting but not directly relevant to the question. It would be more helpful to focus on providing a clear and succinct explanation for why the chosen option is the most appropriate."}, {"output": "The model-generated answer provides a comprehensive overview of the potential consequences of ignorance between groups. However, there are a few areas that could be improved:\n\n1. Lack of prioritization: The answer lists all the options without indicating which one is the most likely or impactful consequence of ignorance between groups. It would be helpful to prioritize or rank the options based on their likelihood or severity.\n\n2. Lack of focus: The answer covers a wide range of potential outcomes, but it could benefit from a more focused approach. It would be more effective to delve deeper into a few key consequences rather than briefly touching on all the options.\n\n3. Lack of actionable suggestions: While the answer outlines the negative outcomes of ignorance between groups, it could be enhanced by providing actionable suggestions for addressing and overcoming ignorance. Including practical steps or strategies for promoting understanding and empathy would make the answer more constructive.\n\n4. Lack of real-world examples: Incorporating real-world examples or case studies could help illustrate the potential consequences of ignorance between groups and make the answer more relatable and impactful.\n\nOverall, the answer would be strengthened by prioritizing the consequences, focusing on a few key outcomes, providing actionable suggestions, and incorporating real-world examples."}, {"output": "The answer provides a thorough analysis of the options and materials available at each location. However, there are a few areas that could be improved:\n\n1. Clarity and organization: The answer could be more organized and concise. It would be helpful to present the analysis in a clearer and more structured manner, perhaps by listing the options and their respective suitability for stool construction.\n\n2. Consideration of practicality: While the answer discusses the availability of materials and tools, it could further consider the practicality and feasibility of building stool legs in each location. For example, it could discuss the potential challenges or restrictions in using materials from certain locations.\n\n3. Addressing safety and regulations: The answer briefly mentions the unlikelihood of prisoners being allowed to build stool legs in a jail, but it could delve deeper into safety and regulatory considerations for each location. This would provide a more comprehensive assessment of the options.\n\n4. Conclusion: The answer could benefit from a clear and definitive conclusion that summarizes the best option and justifies the choice based on the analysis provided.\n\nOverall, the answer provides valuable insights but could be enhanced by improving clarity, considering practicality, addressing safety and regulations, and providing a clear conclusion."}, {"output": "The model-generated answer provides some relevant information about the different options for shopping in a dense urban area. However, there are several critical points to address:\n\n1. Redundancy: The answer repeats the same information for \"Downtown\" and \"City Center,\" which is unnecessary and could be consolidated.\n\n2. Lack of specificity: The answer could benefit from providing specific examples of cities or areas known for their shopping districts, rather than using generic terms like \"downtown\" or \"city center.\"\n\n3. Inaccurate information: The mention of \"elevator\" and \"workplace\" as options for shopping is misleading and not relevant to the question. The answer should focus solely on viable shopping locations within a dense urban area.\n\n4. Lack of consideration for accessibility: While the answer mentions that malls are far away from cities, it does not address the accessibility of downtown areas for individuals who may have mobility challenges or other accessibility needs.\n\n5. Missing considerations: The answer does not address factors such as parking availability, public transportation access, or the overall shopping experience in each location.\n\nSuggestions for improvement:\n\n- Provide specific examples of well-known shopping districts or areas within dense urban environments.\n- Remove irrelevant options such as \"elevator\" and \"workplace\" from the response.\n- Consider factors such as accessibility, parking, and public transportation when discussing shopping locations.\n- Offer insights into the unique shopping experiences offered by each location, such as the variety of stores, cultural attractions, or local specialties."}, {"output": "The model-generated answer provides some relevant information, but there are several areas that could be improved:\n\n1. Lack of specificity: The answer does not choose a specific option from the given list and justify the choice. It should explicitly select one of the options and explain why it is the correct choice.\n\n2. Incomplete justification: The answer mentions using a ladder in a garage, hardware store, or similar building, but it does not explain why these locations specifically require a ladder for eating. The justification for choosing a specific option should be more detailed and specific.\n\n3. Confusing information: The answer mentions using a ladder to climb an apple tree, which is not a location from the given options. This is irrelevant and adds confusion to the response.\n\n4. Lack of clarity: The explanation for using a ladder to reach a higher level is vague and does not provide a clear rationale for why it is unlikely to be needed.\n\nSuggestions for improvement:\n- Choose a specific option from the given list and provide a clear and detailed justification for why a ladder would be needed for eating in that location.\n- Remove irrelevant information about climbing an apple tree and focus on the given options.\n- Provide a more thorough and specific explanation for why a ladder may or may not be needed in each location."}, {"output": "The model's answer contains some critical comments and helpful suggestions. Here are the specific critiques for the model-generated answer:\n\n1. The model correctly identifies the logical fallacy of affirming the consequent, but it could provide a more detailed explanation of why this is a fallacy in the context of the premise and hypothesis.\n\n2. The model should emphasize that the premise alone does not provide sufficient evidence to directly imply the hypothesis. It should suggest that further investigation or evidence is needed to establish a causal link between the family's presence in the house and their deaths during the landslide.\n\n3. The model could provide a clearer distinction between the implications of the premise (existence of the family and their presence in Crawford Notch) and the hypothesis (the cause of their deaths).\n\n4. The model should offer a more balanced perspective by acknowledging that while the premise implies the family's existence and presence in the area, it does not definitively prove the cause of their deaths.\n\n5. The model should encourage critical thinking by prompting the reader to consider alternative explanations for the family's deaths, rather than solely focusing on the landslide as the cause.\n\nOverall, the model's answer could benefit from a more thorough and nuanced analysis of the logical implications of the premise and hypothesis, as well as a clearer distinction between correlation and causation."}, {"output": "The model-generated answer provides a general explanation of the difference between a premise and a hypothesis, as well as the scientific process of formulating and testing hypotheses. However, the critique should address the specific question about whether the given premise implies the hypothesis. The critique should also provide more specific feedback on how the answer could be improved, such as by directly addressing the relationship between the premise and the hypothesis in the given example. Additionally, the critique could suggest providing a more concise and focused response that directly addresses the question asked."}, {"output": "The model's reasoning is flawed and lacks critical analysis. Here are some specific critiques and suggestions for improvement:\n\n1. The model fails to consider the possibility that the friend could have replaced Stan's blue beanie with a different hat before returning it the next day. It should acknowledge the ambiguity in the premise and not make assumptions about the color of the hat.\n\n2. The model's reasoning is overly simplistic and does not account for alternative interpretations of the premise. It should consider different scenarios and provide a more nuanced analysis.\n\n3. The model's explanation lacks depth and fails to address the potential complexities of the situation. It should explore the implications of the premise more thoroughly and consider all possible outcomes.\n\n4. The model should provide a more comprehensive justification for its answer by considering different perspectives and potential counterarguments. It should acknowledge the limitations of the information provided and avoid making definitive statements without sufficient evidence.\n\nOverall, the model's response needs to be more critical, thorough, and open-minded in its analysis of the premise and hypothesis. It should consider multiple interpretations and provide a more balanced and nuanced evaluation."}, {"output": "The model-generated answer provides a general explanation of premises, hypotheses, and logical reasoning, but it does not directly address whether the given premise implies the hypothesis. Here are some specific critiques and suggestions for improvement:\n\n1. Lack of Direct Analysis: The answer does not directly analyze whether the premise implies the hypothesis. It should explicitly evaluate whether the information about the composer's lifespan supports or contradicts the hypothesis that he died young.\n\n2. Relevance to the Topic: The discussion about the validity of arguments and the examination of arguments independently of their content is not directly related to the specific question about the premise and hypothesis. The answer should focus on the relationship between the premise and the hypothesis in the given context.\n\n3. Clarity and Conciseness: The explanation could be more concise and focused on addressing the specific question. It should avoid unnecessary tangents and provide a clear and direct analysis of the premise and hypothesis.\n\n4. Specific Examples: The answer could benefit from specific examples related to the composer's lifespan and the concept of \"dying young\" to illustrate the analysis of the premise and hypothesis.\n\n5. Conclusion: The answer should conclude with a clear determination of whether the given premise implies the hypothesis, based on the specific information provided about the composer's lifespan.\n\nOverall, the answer would be improved by directly addressing the relationship between the premise and hypothesis, providing relevant examples, and maintaining a clear and focused explanation."}, {"output": "The critique for the model-generated answer could be:\n\n1. The critique should point out that the model's response lacks a clear explanation of why the premise does not imply the hypothesis. It should suggest providing specific reasons or evidence to support the conclusion.\n\n2. The critique should highlight that the model's counterexample is not directly relevant to the premise and hypothesis provided. It should recommend using a counterexample that directly addresses the relationship between St. John Richardson Liddell and the Southern identity.\n\n3. The critique should encourage the model to provide a more thorough analysis of St. John Richardson Liddell's background and actions to determine whether the premise implies the hypothesis. This could involve examining Liddell's beliefs and actions related to the Southern identity and slavery."}, {"output": "The critique for the model-generated answer is as follows:\n\n1. Lack of Clarity: The explanation of the relationship between the premise and the hypothesis is unclear. The answer should provide a more direct and specific analysis of whether the hypothesis is implied by the given premise.\n\n2. Justification: The answer lacks a clear justification for why the premise implies the hypothesis. It should provide a more detailed and logical reasoning to support the conclusion.\n\n3. Ambiguity: The answer uses vague language such as \"certainly implied\" without providing concrete evidence or examples from the premise to support this claim. It should use specific details from the premise to demonstrate how the implication is derived.\n\n4. Improvement Suggestion: The answer could benefit from providing a structured breakdown of the premise and how it logically leads to the hypothesis. It should also consider addressing any potential counterarguments or alternative interpretations to provide a more comprehensive analysis."}, {"output": "The critique for the model-generated answer is as follows:\n\n1. The answer lacks specificity in explaining why the premise does not imply the hypothesis. It should provide a clear and detailed explanation to justify the conclusion.\n\n2. The answer could benefit from providing more context about Chuck Wright's background and career to support the justification for why the premise does not imply the hypothesis.\n\n3. The answer should suggest providing evidence or additional information from reliable sources to clarify Chuck Wright's birth year and its relation to his involvement with Quiet Riot. This would strengthen the argument for why the premise does not imply the hypothesis."}, {"output": "The model-generated answer provides a clear explanation of why the premise does not imply the hypothesis. However, it could be improved by providing a more direct and concise response to the question. Additionally, the answer could benefit from offering specific examples or evidence to support its reasoning. It would be helpful to suggest that the answer could be improved by providing a more thorough analysis of the relationship between the premise and the hypothesis, and by offering alternative ways to approach the problem, such as considering the specific measurements of the park."}, {"output": "The critique for the model-generated answer is as follows:\n\n1. The critique should point out that the model correctly identifies that the premise does not imply the hypothesis, as the premise describes a broader category of sauces made from tomatoes, while the hypothesis specifically refers to three specific sauces.\n\n2. The critique should highlight that the model effectively explains that the premise encompasses a larger set of sauces, which may include additional types beyond Neapolitan sauce, tomato sauce, and Salsa di pomodoro.\n\n3. The critique should suggest that the model could provide more specific examples or evidence to support the possibility that the three specific sauces may differ in taste, texture, or other properties due to different recipes. This would strengthen the explanation and make it more convincing.\n\n4. The critique should encourage the model to offer a more detailed analysis of the differences between Neapolitan sauce, tomato sauce, and Salsa di pomodoro, such as their traditional uses, regional variations, or specific ingredients, to further illustrate the potential distinctions between them. This would enhance the depth of the explanation."}, {"output": "The critique for the model-generated answer is as follows:\n\n1. The answer correctly states that the premise does not imply the hypothesis, which is good.\n\n2. The answer could be improved by providing a more detailed explanation of why the premise does not imply the hypothesis. It could mention that the birth date of Paul Albert Raymond Barlatier de Mas is not provided in the premise, and therefore, it is not possible to determine if he was born about 3 weeks before Halloween.\n\n3. The answer could also suggest that in order to determine the accuracy of the hypothesis, additional information about Paul Albert Raymond Barlatier de Mas's birth date would be needed.\n\n4. The answer could be more concise and to the point, avoiding unnecessary repetition."}, {"output": "The critique for the model-generated answer is as follows:\n\n1. The definition of a mall provided is accurate, but the critique should also consider other aspects such as the purpose and function of a mall, not just the physical layout.\n\n2. The critique should address the specific characteristics of The Forum Shops at Caesars and how they align or differ from the typical features of a mall.\n\n3. The critique should provide more detailed evidence or examples to support the argument that The Forum Shops may not fit the traditional definition of a mall.\n\n4. The critique should suggest exploring alternative definitions or perspectives on what constitutes a mall to provide a more comprehensive analysis.\n\n5. The critique should encourage considering the broader context of the shopping environment and consumer experience in relation to the classification of The Forum Shops as a mall."}, {"output": "The model's answer contains some critical points, but there are areas for improvement. Here are some specific critiques and suggestions:\n\n1. Lack of clarity: The answer lacks a clear and direct response to the question. It should explicitly state whether the premise implies the hypothesis or not.\n\n2. Ambiguity: The answer introduces additional scenarios that are not relevant to the original premise and hypothesis. It should focus on analyzing the given premise and hypothesis without introducing unrelated possibilities.\n\n3. Lack of reasoning: The answer does not provide a clear logical analysis of why the premise does or does not imply the hypothesis. It should explain the logical connection or lack thereof between the premise and the hypothesis.\n\n4. Improvement suggestion: The answer should provide a direct and concise analysis of how the given premise does or does not imply the hypothesis, based on the information provided.\n\n5. Clarity and precision: The answer should be more focused and provide a clear justification for the conclusion, avoiding unnecessary speculation.\n\nOverall, the answer would benefit from a more focused and logically structured analysis of the given premise and hypothesis, without introducing irrelevant scenarios."}, {"output": "The model's answer provides a thorough explanation of the potential factors that could influence the hypothesis, which is helpful. However, the critique could be improved by providing more specific examples or scenarios to illustrate how additional information could impact the implication of the hypothesis. Additionally, the critique could suggest that the answer could be more concise and focused on directly addressing the premise and hypothesis, rather than delving into hypothetical scenarios. Lastly, the critique could encourage the model to provide a clearer and more direct justification for why the premise does not imply the hypothesis, perhaps by highlighting specific missing information that would be necessary to make a definitive conclusion."}, {"output": "The critique for the model-generated answer is as follows:\n1. The explanation lacks clarity and conciseness. It could be more straightforward and to the point.\n2. The answer could benefit from providing a more direct and explicit justification for why the premise does not imply the hypothesis.\n3. It would be helpful to include specific examples or scenarios from the premise to illustrate why the hypothesis cannot be inferred.\n4. The answer could be improved by suggesting alternative interpretations of the premise that do not necessarily lead to the hypothesis."}, {"output": "The model-generated answer contains a detailed explanation of logical implication and the need for additional assumptions or background knowledge to make a valid inference. However, the response could be improved in the following ways:\n\n1. Clarity: The explanation is quite technical and may be difficult for a general audience to understand. It would be helpful to simplify the language and provide more concrete examples to illustrate the point.\n\n2. Relevance: The response delves deeply into formal logic and propositional formalization, which may not be necessary for addressing the question at hand. It would be more effective to focus on directly addressing whether the hypothesis follows from the given premise.\n\n3. Conciseness: The answer is quite lengthy and could benefit from being more concise and to the point. It's important to provide a clear and direct response to the question without unnecessary elaboration.\n\n4. Practical Application: While discussing formal logic is valuable, it would be beneficial to include practical examples or real-world scenarios to demonstrate the concept of logical implication and the need for additional assumptions.\n\n5. User-Focused: The response should be tailored to the specific question and provide a clear justification of whether the hypothesis follows from the premise, rather than delving into general concepts of logical implication."}, {"output": "The critique for the model-generated answer could include the following points:\n1. The explanation lacks clarity and precision in addressing the relationship between the premise and the hypothesis.\n2. The response could benefit from providing a more detailed analysis of how the premise does not necessarily imply the hypothesis.\n3. It would be helpful to include examples or scenarios to illustrate why the premise alone is not sufficient to support the hypothesis.\n4. The critique should suggest providing a more thorough examination of the context and potential interpretations of the premise to strengthen the explanation."}, {"output": "The model's answer provides a general explanation of the relationship between a premise and a hypothesis, but it does not directly address the specific premise and hypothesis provided in the question. It would be more helpful to directly apply the concept to the given scenario of a black woman in a pink shirt, eating a muffin, selling ducks and other toys at a special event. Additionally, the answer could benefit from providing a more focused and concise explanation of how the given premise does or does not imply the hypothesis, rather than using a different example. Lastly, the answer could be improved by including specific criteria or factors that would determine whether the given premise implies the hypothesis or not."}, {"output": "The model's answer contains some valid points, but there are several critical comments and helpful suggestions to consider:\n\n1. Clarity: The answer is overly technical and may be difficult for a general audience to understand. It would be helpful to simplify the language and provide a more accessible explanation.\n\n2. Relevance: The answer focuses heavily on the physics of the dog's movement, but it does not directly address whether the premise implies the hypothesis. It would be beneficial to directly connect the physics explanation to the relationship between the premise and the hypothesis.\n\n3. Assumptions: The model introduces unspoken assumptions, which may not be relevant to the original premise and hypothesis. It would be more effective to stick to the specific scenario provided and avoid introducing additional assumptions.\n\n4. Conciseness: The answer is quite lengthy and could benefit from being more concise and to the point. It would be helpful to streamline the explanation and focus on the key elements relevant to the premise and hypothesis.\n\n5. Clarity of implication: The explanation of how the premise implies the negated hypothesis could be clearer. It would be beneficial to provide a more straightforward and explicit connection between the two.\n\nOverall, the model's answer would benefit from simplifying the language, directly addressing the relationship between the premise and hypothesis, sticking to the specific scenario provided, being more concise, and providing a clearer explanation of implication."}, {"output": "The model's answer contains some helpful insights, but there are a few critical points to consider. Firstly, the response could benefit from a clearer structure and organization to enhance readability and coherence. Additionally, the explanation could be more concise and focused on directly addressing the implication of the premise on the hypothesis. It would be beneficial to provide a more direct and explicit justification for the implication or lack thereof, rather than delving into various interpretations and assumptions. Lastly, the response could be improved by offering specific examples or evidence to support the analysis, which would strengthen the overall argument."}, {"output": "The critique for the model-generated answer is as follows:\n\n1. Lack of clarity: The explanation provided lacks clarity in explaining why the premise does not imply the hypothesis. It would be helpful to provide a more detailed and specific explanation to support the conclusion.\n\n2. Incomplete reasoning: The answer could benefit from providing a more comprehensive analysis of how the premise and hypothesis are related. It should address the specific elements of the premise and how they do not necessarily lead to the conclusion of the hypothesis.\n\n3. Lack of examples: The answer could be improved by providing specific examples or scenarios to illustrate why the premise does not necessarily imply the hypothesis. This would help in strengthening the argument and making it more convincing.\n\n4. Suggestions for improvement: It would be beneficial to suggest ways in which the premise could be modified or expanded to better support the hypothesis. Additionally, providing alternative interpretations of the premise and how they do not lead to the hypothesis would enhance the answer."}, {"output": "The critique for the model-generated answer is as follows:\n\n1. Lack of Clarity: The explanation provided is overly verbose and lacks clarity. It should be more concise and to the point to effectively convey the reasoning.\n\n2. Lack of Focus: The response goes off on tangents about the man's potential actions and the park's potential features, which are not directly relevant to the premise and hypothesis.\n\n3. Lack of Directness: The critique lacks directness in addressing the specific relationship between the premise and hypothesis. It should clearly state whether the premise implies the hypothesis or not.\n\n4. Lack of Structure: The critique should be structured in a more organized manner, with clear sections addressing the specific aspects of the model-generated answer that need improvement.\n\n5. Lack of Justification: The critique should provide specific examples or reasoning to support the suggested improvements, rather than just stating the shortcomings.\n\n6. Lack of Actionable Suggestions: The critique should include actionable suggestions for how the model-generated answer can be improved, such as providing a more focused and direct response with clear reasoning."}, {"output": "The critique for the model-generated answer could be:\n\n1. The answer lacks a clear explanation of why the premise does not imply the hypothesis. It should provide a more detailed analysis of the relationship between the premise and the hypothesis.\n\n2. The counterexample provided is not directly related to the premise and hypothesis given. It would be more effective to provide a counterexample that directly addresses the relationship between \"Dad and a child lay outdoors on a blanket\" and \"Two people lying on a blanket.\"\n\n3. The answer could benefit from providing a more thorough and logical reasoning to support the conclusion that the premise does not imply the hypothesis. It should include a breakdown of the specific elements in the premise that do not necessarily lead to the conclusion of \"Two people lying on a blanket.\""}, {"output": "The model-generated answer lacks a comprehensive summary of the article. It fails to mention the specific location of the collision (A5 in Capel Curig in Snowdonia), the involvement of the fire service apprentice mechanic, and the expressions of support from the North Wales chief fire officer and the police appeal for witnesses. The critique should include the following points:\n1. The summary should include specific details such as the location of the collision, the individuals involved, and the expressions of support from relevant authorities.\n2. The summary should provide a clear overview of the events leading to the collision and its impact on the community.\n3. The summary should accurately reflect the key information from the article, including the involvement of the fire service apprentice mechanic and the appeal for witnesses by the North Wales Police.\n4. The summary should be more comprehensive and provide a balanced representation of the article's content."}, {"output": "1. The summary lacks specific details from the article, such as the type of aircraft involved (Antonov AN-26), the location of the crash (Loma de la Pimienta mountain), and the previous major plane crash in Cuba in 2010.\n\n2. The summary does not provide any information about the ongoing investigation into the cause of the crash, which is an important aspect of the article.\n\n3. The summary could be improved by including more context about the flight's origin and destination, as well as any additional relevant details about the crash.\n\n4. It would be helpful to mention the significance of this event in the context of aviation safety in Cuba and any potential implications for future air travel in the region."}, {"output": "The model-generated answer lacks a comprehensive summary of the article. It fails to provide important details such as the individual's name (Mohammed Allan), his alleged affiliation with the Islamic Jihad militant group, the controversy surrounding force-feeding of hunger strikers, and the conflicting reports about his medical condition. The critique should highlight the need for a more detailed and accurate summary that encompasses the key points of the article. Additionally, the critique should suggest including information about the ethical and legal debates surrounding force-feeding, the stance of Israeli doctors and the Israeli Medical Association, and the potential implications of the case as a test of a recent Israeli law. The critique should emphasize the importance of providing a balanced and comprehensive summary that captures the complexity of the situation."}, {"output": "The model-generated answer is not a comprehensive summary of the article. It fails to capture the key details such as the name of the tourist attraction (The Gobbins), the location (Islandmagee, County Antrim), the reason for closure (damage from storms and record rainfall), the plans for restoration, and the history of the attraction. The critique should highlight the need for a more detailed and accurate summary that includes all the important information from the article. Additionally, the critique should suggest that the summary should provide a clear and concise overview of the main points, ensuring that it accurately represents the content of the original article."}, {"output": "The model-generated answer does not provide a summary of the article as requested. It fails to mention the specific improvements, such as the pedestrian and cycle bridge across the A63, extensions to the city's art gallery and theatre, and the hosting of the Turner Prize in 2017. Additionally, the answer lacks details about the funding allocation and the significance of these improvements in the context of Hull being awarded the title of City of Culture. To improve, the summary should include specific details about the planned improvements, their impact on the city, and the timeline for their implementation."}, {"output": "The model-generated answer does not accurately summarize the article. It fails to mention the specific details of the raids, the nationalities of those arrested, the seizure of fake passport machines, and the tightening of security ahead of the Southeast Asian Games. The summary also lacks clarity and conciseness. To improve, the summary should include all the key details from the article and be presented in a clear and concise manner. Additionally, the summary should focus on providing an accurate and comprehensive overview of the events described in the article."}, {"output": "The model-generated answer does not accurately summarize the article. It fails to mention the specific details about the worst-affected state, Puebla, and the significant impact of the storm on the region. It also overlooks the details about the number of confirmed deaths and the areas affected by the storm. To improve the summary, it should include the specific number of confirmed deaths, the impact on different states, and the ongoing search and rescue efforts. Additionally, the summary should highlight the warning from the US National Hurricane Center about another approaching storm, Tropical Storm Javier, and its potential impact on Mexico."}, {"output": "The model-generated answer does not accurately summarize the article. It fails to mention the specific location (Deerhurst House on Haymerle Road in Peckham), the involvement of the police, the age of the victim, the response by paramedics, and the pending post-mortem examination. Additionally, it incorrectly states that the man was stabbed to death, when the cause of death is not specified in the article. The summary should be revised to include these important details and to avoid making assumptions about the cause of death."}, {"output": "The summary lacks specific details from the article, such as the name of the company responsible for the plastic bottles, the product involved, and the circumstances surrounding the incident. It would be helpful to include more information about the environmental impact and the cleanup efforts. Additionally, the summary could benefit from providing a clearer timeline of events and the potential consequences of the lost container. To improve, the summary should aim to capture the key details and implications of the article in a more comprehensive manner."}, {"output": "The model-generated answer is accurate and concise, but it lacks a summary of the entire article. It only focuses on one aspect of the article, which is Tony Blair's denial of seeking a role as the US President's Middle East envoy. The critique should include a suggestion to provide a more comprehensive summary that covers the key points of the article, such as the context of the denial, the background of Tony Blair's involvement in the peace process, and his recent campaign regarding the Brexit referendum. Additionally, the critique should suggest expanding the summary to include more details about the reported meeting with Jared Kushner and the purpose of the discussions at the White House."}, {"output": "1. The summary lacks important details from the article such as the impact on the 150 employees working at the office in Dundalk, County Louth, and the formal consultation process with the employees.\n2. The summary does not mention the reason for the closure, which is the decision to separate eBay and its PayPal business, and the subsequent request from PayPal for eBay to vacate the facility.\n3. The summary could be improved by including information about PayPal's commitment to its offices in Dundalk and Dublin and its plans to expand and occupy the office space vacated by eBay.\n4. The summary should provide a more comprehensive overview of the article, including the timeline for the closure and any potential implications for the local community and economy."}, {"output": "The model-generated answer provides a brief summary of the article but lacks depth and context. It fails to mention the involvement of Twitter and its board, including Chief Executive Parag Agrawal, as defendants in the lawsuit. Additionally, it does not address the lawsuit's specific claims, such as the breach of fiduciary duties by Twitter directors. To improve the summary, it should include these key details to provide a comprehensive overview of the situation. Furthermore, the critique could suggest expanding on the potential implications of the lawsuit and the broader impact on both Elon Musk and Twitter. This would help provide a more thorough and informative summary for the reader."}, {"output": "The critique for the model-generated answer is as follows:\n1. The summary lacks specific details about the car crash, such as the cause of the accident or any relevant circumstances.\n2. The summary does not provide information about the response from the local authorities or emergency services following the crash.\n3. It would be helpful to include details about any ongoing investigations or safety measures being taken in response to the incident.\n4. The summary should mention the impact on the local community and any support services being offered to those affected by the tragedy.\n5. The summary could benefit from including information about any potential road safety initiatives or campaigns in the area."}, {"output": "The model-generated answer does not provide a comprehensive summary of the article. It lacks important details such as the hospital's apology, the victim's experience, and the actions taken by the hospital trust. It also fails to mention the review findings and recommendations. To improve, the summary should include these key points to provide a more thorough and accurate overview of the article."}, {"output": "The model-generated answer provides a brief summary of the article, but it lacks specific details and fails to capture the full scope of the situation. Here are some critiques and suggestions for improvement:\n\n1. Lack of Specifics: The summary does not mention the name of the council (Sandwell Council) or the region (West Midlands), which are crucial details for understanding the context of the article. The summary should include these specific details to provide a comprehensive overview.\n\n2. Incomplete Information: The summary does not cover the key points of the article, such as the multiple leadership changes, misconduct allegations, and the government's concerns about poor behavior and lack of trust within the council. It should include these important details to provide a more comprehensive summary.\n\n3. Lack of Objectivity: The summary does not maintain an objective tone and fails to present a balanced view of the different perspectives mentioned in the article. It should strive to present a neutral and balanced summary of the various viewpoints expressed in the article.\n\n4. Improvement Suggestions: The summary could be improved by including specific examples of the alleged misconduct, the responses from different political figures, and the potential impact of the government's proposed intervention. Providing these specific details would enhance the comprehensiveness and accuracy of the summary."}, {"output": "The critique for the model-generated answer is as follows:\n\n1. The summary lacks specific details from the article, such as the total number of migrants brought into Dover, the number of boats used, and the actions taken by the Home Office and French authorities.\n\n2. The summary does not provide a clear understanding of the reasons behind the increase in crossings, such as the impact of warmer weather and calm seas.\n\n3. The summary does not mention the government's response to the situation, including the actions taken by the minister for tackling illegal migration and the Nationality and Borders Bill.\n\n4. The summary should include the challenges and risks faced by the migrants during the crossing, as well as the impact on UK taxpayers and the government's ability to help refugees through legal routes.\n\n5. The summary should also address the criticisms and protests surrounding the government's borders bill, as well as the defeats it has suffered in the House of Lords.\n\nSuggestions:\n- Include specific numbers and details from the article to provide a comprehensive summary.\n- Explain the factors contributing to the increase in crossings, such as weather conditions and government responses.\n- Address the challenges and risks faced by migrants, as well as the impact on UK taxpayers and the government's ability to help refugees through legal routes.\n- Discuss the criticisms and protests surrounding the government's borders bill and its defeats in the House of Lords."}, {"output": "The critique for the model-generated answer is as follows:\n\n1. Lack of Summary: The model's answer fails to provide a concise summary of the article. It should have included key details such as the protesters' opposition to the construction of the high-speed railway, their resistance to eviction, and their tactics to make eviction more difficult.\n\n2. Incomplete Information: The model's answer does not mention the protesters' construction of tree houses, digging a tunnel, and plans to build a tower using wooden pallets, which are crucial details from the article.\n\n3. Lack of Context: The model's answer does not provide context about the reasons behind the protesters' opposition to the high-speed railway, which is essential for understanding the situation.\n\n4. Missing Stakeholder Perspectives: The model's answer does not include the perspective of HS2 Ltd, which is important for providing a balanced summary of the article.\n\nSuggestions:\n- The summary should include the protesters' reasons for opposing the high-speed railway and their concerns about environmental impact.\n- Include details about the protesters' tactics to resist eviction, such as building tree houses and digging a tunnel.\n- Provide a balanced summary by including perspectives from both the protesters and HS2 Ltd.\n- Ensure the summary is concise and captures the key points of the article."}, {"output": "The summary provided is accurate, but it lacks depth and context. It fails to mention the crucial details such as the circumstances of Naomi Irion's disappearance, the description of the suspect, and the plea for information from the FBI. Additionally, it does not address the urgency and seriousness of the situation. To improve the summary, it should include a more comprehensive overview of the events, emphasize the need for public assistance, and highlight the potential impact of finding Naomi Irion."}, {"output": "The model-generated answer does not provide a summary of the article. It fails to mention the arrest of a 27-year-old man on suspicion of grievous bodily harm, the location of the incident, the time of the incident, and the police appeal for witnesses. It only focuses on the injured man's condition. To improve, the summary should include all the key details from the article, such as the arrest, location, time, injuries, and police appeal for witnesses. Additionally, the summary should be more comprehensive and provide a clear overview of the entire incident."}, {"output": "The model-generated answer provides a brief summary of the article but lacks specific details and context. Here are some critiques and suggestions for improvement:\n1. Lack of specific details: The summary does not mention the discovery of four separate shipments, the locations where the shipments were intercepted (Cincinnati, New York, and Philadelphia), the contents of the shipments, and the concealment tactics used by the traffickers.\n2. Contextual information: The summary does not include important contextual information such as the weight of cocaine in each shipment, the collaboration between CBP officers across different locations, and the impact of the seizures on drug trafficking operations.\n3. Incomplete summary: The summary should provide a more comprehensive overview of the article, including the actions taken by CBP officers, the testing process for identifying cocaine, and the statement from Joseph Martella, CBP's area port director in Philadelphia.\nSuggestions for improvement:\n- Include specific details about the contents of the shipments, the concealment tactics used, and the weight of cocaine in each shipment.\n- Provide contextual information about the collaboration between CBP officers, the impact of the seizures on drug trafficking, and the testing process for identifying cocaine.\n- Expand the summary to include the actions taken by CBP officers and the statement from Joseph Martella, CBP's area port director in Philadelphia."}, {"output": "1. The model-generated answer is inaccurate as it mentions the Ebola outbreak in West Africa, which is not relevant to the article about Jersey's Safer Travel Policy being suspended.\n\n2. The summary does not capture the key points of the article, such as the removal of testing and isolation for arriving passengers, the phasing out of Covid-19 restrictions by 31 March, and the advice for outbound passengers to check travel requirements.\n\n3. The summary lacks clarity and coherence, making it difficult for the reader to understand the main message of the article.\n\n4. The summary should focus on providing a clear and accurate overview of the article's content, rather than introducing unrelated information.\n\n5. It would be helpful to include specific details about the changes in travel policy and the reasons behind the decision, as well as any potential implications for travelers."}, {"output": "The model-generated answer does not provide a summary of the article. It only focuses on one aspect of the project, the VR experience, and does not cover the broader scope of the project. It would be helpful to include a summary of the entire article, highlighting the key points such as the project's objectives, the involvement of researchers, the significance of the caves, and the expected impact on the city's regeneration. Additionally, the critique could suggest expanding the summary to encompass the various aspects of the project and its potential implications for Nottingham's historical and cultural significance."}, {"output": "The answer provided contains some relevant information about how plants take in water and minerals from the soil, and how certain crops may not get enough iron from the soil, leading to the need for iron fertilizer. However, the answer does not directly address the specific question of which parts of the plants take in iron. \n\nHere are some specific critiques and suggestions for improvement:\n1. The answer should explicitly state that roots are the parts of the plants that take in iron, as this directly addresses the question asked.\n2. The answer could provide more specific details about how roots absorb iron from the soil, such as through specialized transport proteins or mechanisms.\n3. The answer could benefit from a more direct and concise explanation of how iron is absorbed by plants, without delving into specific examples of crops that may not get enough iron from the soil.\n\nOverall, the answer should focus on directly addressing the question and providing a clear and concise explanation of how plants absorb iron through their roots."}, {"output": "The answer provided does not directly address the question asked, which is about which mixture would be easiest to separate. The answer should focus on comparing different mixtures and explaining why one would be easier to separate than the others. Additionally, the answer could benefit from providing more specific examples of the physical methods that could be used to separate the components of the fruit salad. It would be helpful to include suggestions for improvement, such as providing a comparison with other mixtures and elaborating on the specific separation techniques that could be applied."}, {"output": "The answer provides a good explanation of the greenhouse effect and the role of different gases in trapping heat. However, the specific question asked which gas accounts for the largest percentage of greenhouse gases in the atmosphere, and the answer does not directly address this. The answer correctly identifies water vapor as the most abundant greenhouse gas but fails to explicitly state that carbon dioxide accounts for the largest percentage of greenhouse gases due to human activities. \n\nTo improve the answer, it should directly address the question by stating that carbon dioxide accounts for the largest percentage of greenhouse gases in the atmosphere due to human activities. Additionally, the answer could benefit from a more concise and direct structure to ensure clarity and focus on the specific question asked."}, {"output": "The answer provided correctly identifies one way in which national parks have a positive effect on ecosystems by limiting human activities such as mining, logging, and hunting. However, the answer could be improved in the following ways:\n\n1. Lack of depth: The answer could benefit from providing more detailed explanations and examples to support the statement. For instance, it could elaborate on how limiting mining, logging, and hunting specifically contributes to the preservation of ecosystems and biodiversity.\n\n2. Incomplete coverage: While the answer mentions the limitation of human activities, it could also address the positive impact of national parks on air and water quality, soil conservation, and the overall ecological balance.\n\n3. Lack of clarity: The answer could be clearer in its structure and organization. It could benefit from presenting the additional ways in which national parks have a positive effect on ecosystems in a more organized and coherent manner.\n\n4. Lack of citations: Including specific examples or references to support the additional ways in which national parks have a positive effect on ecosystems would enhance the credibility of the answer.\n\n5. Suggestions for improvement: The answer could be improved by providing actionable suggestions for how national parks can further enhance their positive impact on ecosystems, such as implementing sustainable management practices and engaging in conservation efforts beyond park boundaries."}, {"output": "The answer provides a valid difference between plants and animals, highlighting the distinction in their nutrition. However, the critique could include the following points:\n1. Elaboration: The answer could benefit from further elaboration on how plants produce their own food through photosynthesis and how animals obtain their food. This would enhance the depth of understanding for the reader.\n2. Clarity: The answer could be clearer by explicitly stating that plants use photosynthesis to produce their own food, which would enhance the precision of the response.\n3. Comparative Analysis: It would be helpful to include a comparative analysis of the implications of this difference, such as how it influences the behavior and characteristics of plants and animals. This would provide a more comprehensive understanding of the topic."}, {"output": "The answer provided is accurate in identifying soil as a nonliving part of a forest ecosystem and explaining its role in providing nutrients and support for living organisms. However, the answer could be improved by providing additional nonliving components of a forest ecosystem, such as rocks, water, and air. Including a more comprehensive list of nonliving components would enhance the completeness of the response. Additionally, the answer could benefit from further elaboration on the specific role of soil in the forest ecosystem, such as its impact on water retention and erosion control. This would provide a more thorough understanding of the importance of soil within the forest environment."}, {"output": "The model-generated answer contains some accurate information about the characteristics of our Milky Way galaxy, such as the number of stars and its approximate size. However, there are some critical points to address:\n\n1. Lack of Clarity: The answer lacks clarity in directly addressing the specific characteristics provided in the question, such as the distance from Earth and the diameter of the group of objects in space.\n\n2. Incomplete Explanation: The answer does not fully explain why the student's description aligns with the Milky Way galaxy. It would be helpful to provide a more detailed explanation of how the characteristics match those of the Milky Way.\n\n3. Missing Context: The answer could benefit from providing additional context about why the student's description is a likely match for the Milky Way galaxy, such as the common characteristics of galaxies and how they compare to the student's description.\n\nSuggestions for improvement:\n\n1. Provide a direct and clear explanation of how each of the student's characteristics align with those of the Milky Way galaxy.\n\n2. Include additional information about the common characteristics of galaxies and how they relate to the student's description to provide a more comprehensive answer.\n\n3. Consider addressing the specific distance from Earth and the diameter of the group of objects in space to further support the conclusion that the student is most likely describing the Milky Way galaxy."}, {"output": "The model-generated answer provides a detailed explanation of how thunder is created and why we hear it after seeing lightning. However, the answer lacks specificity in addressing the direct cause of the windows rattling. It would be helpful to explicitly state that the pressure waves from the thunder cause the windows to vibrate, leading to the rattling sound. Additionally, the answer could benefit from simplifying the language and focusing on directly addressing the question without unnecessary details about the speed of light and sound. Lastly, it would be beneficial to include practical suggestions for mitigating the rattling of windows during thunderstorms, such as using soundproofing materials or securing the windows."}, {"output": "The answer provided does not directly address the specific unit that the researcher would use when determining the mass of the fish in the context of the investigation. Here are some specific critiques and suggestions for improvement:\n\n1. Lack of direct answer: The answer does not explicitly state the unit that the researcher would use when determining the mass of the fish. It would be more helpful to directly address the question by stating the appropriate unit for measuring the mass of the fish in the given scenario.\n\n2. Tangential information: The answer delves into the distinction between mass and weight, as well as the impact of water pressure on weight measurement. While this information is relevant, it does not directly answer the question about the specific unit for measuring fish mass in the research context.\n\n3. Lack of clarity: The answer could be improved by providing a clear and concise response that directly addresses the question without introducing additional concepts that may distract from the main point.\n\nSuggestions for improvement:\n- Provide a direct and specific response to the question by stating the unit that the researcher would use when determining the mass of the fish in the given investigation, such as \"The researcher would use grams to measure the mass of the fish.\"\n- If additional information about the impact of water pressure on weight measurement is deemed relevant, it should be presented after addressing the specific unit for measuring fish mass to avoid confusion."}, {"output": "The answer provides a comprehensive explanation of the composition of stars, including the predominant elements and their formation. However, there are a few areas that could be improved:\n\n1. Clarity: The answer is quite detailed and may be overwhelming for someone seeking a basic understanding of star composition. It could benefit from a more concise and simplified explanation.\n\n2. Structure: The answer lacks a clear structure, making it difficult for the reader to follow the flow of information. Breaking it down into distinct sections or using bullet points could enhance readability.\n\n3. Engagement: The answer could be more engaging by incorporating visual aids, such as diagrams or charts, to illustrate the concepts being discussed.\n\n4. Language: While the answer is informative, it could be more engaging by using simpler language and avoiding overly technical terms, or by providing explanations for these terms.\n\n5. Conclusion: The answer could benefit from a brief summary or conclusion to reinforce the key points about star composition."}, {"output": "The model-generated answer provides a general explanation of how camouflage patterns help babies hide from predators. However, the specific example of baby deer's spots as an adaptation for defense from predators is not fully addressed. Here are some critiques and suggestions for improvement:\n\n1. Lack of specificity: The answer does not directly address the example of baby deer's spots as an adaptation for defense from predators. It would be more helpful to explicitly discuss how the spots on baby deer serve as a form of camouflage to protect them from predators.\n\n2. Incomplete explanation: The answer briefly mentions that as the baby deer grows, it develops other means of defense, such as speed. However, it would be beneficial to provide a more comprehensive explanation of how the spots specifically contribute to the deer's defense from predators.\n\n3. Additional details: The answer could be enhanced by including specific details about how the spots on baby deer help them blend into their environment and avoid detection by predators.\n\n4. Clarity and organization: The answer could benefit from a clearer structure and organization to ensure that the information is presented in a logical and coherent manner.\n\nTo improve the answer, it would be beneficial to provide a more focused and detailed explanation of how the specific adaptation of baby deer's spots serves as a defense mechanism against predators. Additionally, including specific examples and elaborating on the role of camouflage in the deer's survival would enhance the overall clarity and depth of the response."}, {"output": "The model-generated answer has several inaccuracies and lacks a clear justification for the chosen option. Here are the specific critiques and suggestions for improvement:\n1. Inaccuracy: The model incorrectly states that social robots can control their emotions. This is not an accurate distinction between social robots and household robots. Social robots are designed to recognize and respond to human emotions, but not necessarily to control their own emotions.\n2. Lack of justification: The answer does not provide a clear justification for choosing option A. It should explain how the ability to recognize and respond to human emotions distinguishes social robots from household robots.\n3. Incomplete explanation: The answer should also mention other key differences between social robots and household robots, such as their roles as companions, their personalized assistance capabilities, and their potential applications in various fields beyond the home.\n\nSuggestions for improvement:\n1. Provide a more accurate distinction: Clearly explain that social robots are designed to recognize and respond to human emotions, making them more personalized and effective in their interactions with users.\n2. Justify the chosen option: Explain how the ability to recognize and respond to human emotions aligns with the capabilities of social robots and distinguishes them from household robots.\n3. Include additional differences: Expand the explanation to include other key differences between social robots and household robots, such as their roles as companions, personalized assistance capabilities, and potential applications in various fields beyond the home."}, {"output": "The model-generated answer is incorrect. The correct answer is (C) when to begin eating. The critique for the model-generated answer could include the following points:\n1. The model incorrectly chose \"what to eat\" as the first important thing to know at a dinner party, which does not align with the provided information about table manners and the proper etiquette at a dinner party.\n2. The model should have paid closer attention to the details provided in the question and the passage, which clearly state that the first important thing to know at a dinner party is when to begin eating, not what to eat.\n3. The model should have provided a more accurate and specific justification for the correct answer, including referencing the information about the hostess serving guests first and herself last, and waiting for the hostess to signal the start of the meal. This would have strengthened the explanation and made it more reliable."}, {"output": "The critique for the model-generated answer is as follows:\n1. Accuracy: The model incorrectly stated that the FBI was set up in the 1920s, specifically in 1935. The FBI was actually established in 1908, not the 1920s.\n2. Lack of precision: The answer should provide the specific year of establishment (1908) rather than a general decade (1920s).\n3. Clarity: The answer could be clearer by stating the exact year of establishment and providing a brief explanation of the significance of the FBI's establishment in that year."}, {"output": "The model-generated answer contains inaccurate information. The first president of Zambia was actually Kenneth Kaunda, who served as the country's president from 1964 to 1991. The answer should be corrected to reflect this historical fact. Additionally, it's important to ensure that the information provided is accurate and verified before presenting it as a response."}, {"output": "The model's answer is accurate in identifying Tom Jones as the original singer of \"Green Green Grass of Home\" and providing some historical context. However, the critique could include the following points:\n1. The answer could be improved by providing more specific details about the song's release and its impact on Tom Jones' career.\n2. It would be helpful to mention the significance of Tom Jones' version in relation to the original recording by Johnny Darrell and how it contributed to the song's popularity.\n3. Adding information about the song's reception, such as chart performance or critical acclaim, would enhance the completeness of the answer."}, {"output": "The critique for the model-generated answer is as follows:\n1. The answer lacks specificity as it does not mention the exact Olympic Games in which big air snowboarding was included as a sport.\n2. It would be helpful to provide additional context or details about the inclusion of big air snowboarding in the Olympics, such as the location or any significant developments related to its inclusion.\n3. The answer could be improved by including information about the significance of big air snowboarding's inclusion in the Olympics and its impact on the sport."}, {"output": "The model-generated answer contains a critical error. The explanation provided for option (A) is inaccurate. The sun's light is not reflected off the clouds when it disappears behind the neighbor's house. Instead, it is refracted by the atmosphere. \n\nTo improve the answer, it should accurately explain the process of refraction and how it causes the sun to appear to slowly disappear behind the neighbor's house. Additionally, the answer should provide a clear justification for why option (B) is the best choice based on the given observation."}, {"output": "The model-generated answer correctly identifies the example of a physical change as lighting a match and provides a brief explanation. However, the critique could include the following points:\n1. Provide more detailed justification: The answer could be improved by providing a more detailed explanation of why lighting a match represents a physical change. This could include discussing the change in state, appearance, or physical properties of the match without altering its chemical composition.\n2. Clarify the distinction between physical and chemical changes: The critique could suggest adding a clarification of the difference between physical and chemical changes to help the reader understand the concept more thoroughly.\n3. Include additional examples: It would be helpful to include additional examples of physical changes to provide a broader understanding of the concept. This could enhance the reader's knowledge and comprehension of the topic."}, {"output": "The model-generated answer provides a correct justification for option C as an example of a physical change but not a chemical change. However, the critique could include the following points:\n1. Clarity: The explanation could be more explicit in distinguishing between physical and chemical changes to ensure a better understanding for the reader.\n2. Elaboration: It would be helpful to provide additional examples or characteristics of physical changes to further support the explanation.\n3. Engagement: The response could be made more engaging by incorporating real-life examples or practical demonstrations to illustrate the concept effectively.\n4. Language: The answer could benefit from using simpler language to ensure accessibility for a wider audience."}, {"output": "The model-generated answer contains some inaccuracies and could be improved in the following ways:\n\n1. Inaccurate explanation: The answer incorrectly states that there are no reflections or refractions of sound in an empty classroom. In reality, sound waves do reflect off the walls, ceiling, and floor, and this reflection contributes to what the student hears.\n\n2. Lack of justification: The answer does not provide a clear justification for why option (A) is the best explanation. It should explicitly explain how an increased loudness of sound is the most relevant factor in this scenario.\n\n3. Missing information: The answer could benefit from including information about the specific characteristics of sound waves that lead to an increased loudness of sound in an empty classroom.\n\nTo improve the answer, it should accurately explain the reflection of sound in an empty classroom, provide a clear justification for why option (A) is the best explanation, and include specific details about the characteristics of sound waves in this scenario."}, {"output": "The model-generated answer correctly identifies option (A) Kevin as the correct option to fill in the underline. However, the justification provided lacks specificity and clarity. It would be helpful to include more detailed reasoning, such as specific examples of Kevin's physical versatility compared to Nick's injury. Additionally, the answer could benefit from a more thorough explanation of why Kevin's physical capabilities make him more versatile than Nick. Providing specific examples and elaborating on the differences in physical abilities would enhance the answer's effectiveness."}, {"output": "The critique for the model-generated answer is as follows:\n1. The justification for choosing option (B) is not clearly explained. It would be helpful to explicitly state that Neil is closer to the door than William, leading to the conclusion that Neil should close it.\n2. The critique should include a suggestion to provide a more detailed explanation of why Neil is the correct option to fill in the underline, such as mentioning the physical proximity of Neil to the door.\n3. The critique should encourage the inclusion of additional context or reasoning to support the choice of option (B) and make the explanation more comprehensive and convincing."}, {"output": "The model-generated answer correctly identifies option D as the best choice. However, the justification provided is not specific or helpful. Here are some critiques and suggestions for improvement:\n1. Lack of detail: The justification lacks specific details about why option D is the best choice. It would be helpful to explain how taking the contacts out and putting them in her eyes is a part of the makeup process.\n2. Clarify reasoning: The justification should provide a clear explanation of why the other options are not suitable for the makeup process. This will help the reader understand the reasoning behind choosing option D.\n3. Provide context: It would be beneficial to provide context about the makeup process and how inserting contacts fits into the overall routine. This will enhance the understanding of why option D is the most appropriate choice.\n\nOverall, the critique should focus on providing specific details, clarifying reasoning, and providing context to improve the justification for choosing option D."}, {"output": "The model-generated answer correctly identifies option C as the best choice. However, the justification provided lacks specificity and depth. Here are some critiques and suggestions for improvement:\n\n1. Lack of Detail: The answer lacks specific details about the man's actions and interactions. It would be helpful to provide more specific observations about the man's behavior and the nature of his interaction with the drummer.\n\n2. Justification Clarity: The justification for choosing option C is not clearly explained. The reasoning behind why the man fading in and out suggests his inconsistent presence or intermittent appearances should be elaborated upon for better clarity.\n\n3. Supporting Evidence: The answer would benefit from providing specific examples or evidence from the scene that support the choice of option C. This could include details about the visual cues that indicate the man's intermittent presence.\n\n4. Improve Explanation: The answer could be improved by explaining how the man fading in and out aligns with the description provided in the question and why it makes option C the most suitable choice.\n\n5. Enhance Coherence: The answer could be enhanced by ensuring that the justification provided for choosing option C is coherent and directly related to the details provided in the question.\n\nOverall, the answer would be strengthened by providing a more detailed and coherent explanation for choosing option C, supported by specific evidence from the scene."}, {"output": "The model-generated answer provides a general explanation of the importance of peat but fails to directly address the question. Here are some specific critiques and suggestions for improvement:\n1. Lack of direct relevance: The answer does not clearly state when peat is an important factor, as requested in the question. It should directly address the specific context in which peat plays a crucial role.\n2. Failure to connect with options: The answer does not attempt to connect the importance of peat to any of the given options. It should provide specific examples or explanations of how peat relates to the options provided.\n3. Lack of actionable information: The answer does not provide actionable information or guidance related to the question. It should offer specific advice or examples related to the importance of peat in a given context.\n\nSuggestions for improvement:\n1. Provide specific examples: The answer should include specific examples of how peat is important in the context of the given options, such as how it relates to soil composition when putting a fresh raspberry seed in soil.\n2. Address the question directly: The answer should clearly state when peat is an important factor, directly addressing the question asked.\n3. Offer actionable advice: The answer should provide actionable advice or information related to the importance of peat in a specific context, such as gardening or agriculture."}, {"output": "The model-generated answer contains a critical error. The answer should be (A) turned food, not (D) pocked oranges. The critique should address the following points:\n1. The answer choice provided is incorrect. The correct answer is (A) turned food, not (D) pocked oranges.\n2. The explanation for the chosen answer is unclear and lacks specificity. It should clearly state that leaving meat on the counter can lead to bacterial growth, which can cause the food to spoil or become unsafe to eat.\n3. The answer could benefit from providing more detailed information about the potential health risks associated with consuming food that has been contaminated by bacteria. This would enhance the completeness of the response."}, {"output": "The critique for the model-generated answer is as follows:\n\n1. Lack of Clarity: The answer does not provide a clear justification for the relationship between Sally and Bob. It should explicitly state the relationship based on the given information.\n\n2. Incomplete Analysis: The answer does not thoroughly analyze the provided information about Sally and Bob's relationship. It should carefully consider the details in the note to determine the correct relationship.\n\n3. Lack of Justification: The answer does not explain why none of the options provided are correct. It should provide specific reasons for rejecting each option and justify the correct relationship between Sally and Bob.\n\n4. Improvement Suggestion: The answer should carefully analyze the details in the note, such as the context of the message and the items being delivered, to determine the most appropriate relationship between Sally and Bob. Additionally, it should provide a clear and justified choice from the given options."}, {"output": "The critique for the model-generated answer is as follows:\n1. Accuracy: The model incorrectly states that calcium carbide is used for the production of calcium cyanamide, while its main industrial use is in the production of acetylene. The model should accurately state that calcium carbide is indeed the raw material for the production of acetylene.\n2. Clarity: The response lacks clarity as it does not clearly explain the main industrial use of calcium carbide, which is the production of acetylene. The model should provide a clear and concise explanation to avoid confusion.\n3. Additional Information: The model should include additional information about the use of calcium carbide in the production of acetylene, such as the chemical reaction involved and its significance in various industries.\n\nSuggestions:\n1. The model should ensure the accuracy of the information provided and verify the facts before generating a response.\n2. The response should be clear and specific, providing accurate details about the main industrial use of calcium carbide in the production of acetylene.\n3. Including additional relevant information, such as the chemical reaction involved in the production of acetylene using calcium carbide, would enhance the completeness of the response."}] --------------------------------------------------------------------------------