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CLEVA: Chinese Language Models EVAluation Platform

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3 | 4 | [![GitHub Repo stars](https://img.shields.io/github/stars/Lavi-Lab/CLEVA)](https://github.com/Lavi-Lab/CLEVA/stargazers) 5 | [![License: CC BY-NC-ND 4.0](https://img.shields.io/badge/License-CC_BY--NC--ND_4.0-blue.svg)](https://creativecommons.org/licenses/by-nc-nd/4.0/) 6 | [![Ask me anything](https://img.shields.io/badge/Ask%20me-anything-blue.svg)](https://github.com/LaVi-Lab/CLEVA/issues/new) 7 | 8 | [🌐Website](http://www.lavicleva.com/) 9 | •[📜Paper \[EMNLP 2023 Demo\]](https://arxiv.org/abs/2308.04813) 10 | •[📌Instructions](#instructions) 11 | •✉️Email 12 | 13 | English | [简体中文](README_zh-CN.md) 14 | 15 |
16 | 17 | ## 🎯 Introduction 18 | 19 | [CLEVA](https://arxiv.org/abs/2308.04813) is a Chinese Language Models EVAluation Platform developed by CUHK [LaVi Lab](https://lwwangcse.github.io/). CLEVA would like to thank Shanghai AI Lab for the great collaboration in the process. The main features of CLEVA include: 20 | - A comprehensive **Chinese Benchmark**, featuring 31 tasks (11 application assessments + 20 ability evaluation tasks), with a total of 370K Chinese test samples (33.98% are newly collected, mitigating *data contamination* issues); 21 | - A standardized **Prompt-Based Evaluation Methodology**, incorporating unified pre-processing for all data and using *a consistent set* of Chinese prompt templates for evaluation. 22 | - A trustworthy **Leaderboard**, as CLEVA uses a significant amount of new data to minimize data contamination and regularly organizes evaluations. 23 | 24 | The leaderboard is evaluated and maintained by CLEVA using new test data. Past leaderboard data (processed test samples, annotated prompt templates, etc.) are made available to users for local evaluation runs. 25 | 26 | ![Overview](overview.png) 27 | 28 | ## 🔥 News 29 | 30 | - **\[2025.05.16\]** C2LEVA has been accepted to [Findings of ACL 2025](https://2025.aclweb.org/). 🔥🔥🔥 31 | - **\[2024.12.06\]** We are thrilled to announce C2LEVA, an effort toward building a comprehensive bilingual benchmark with systematic contamination prevention. 32 | - **\[2023.11.02\]** Thanks for the support of Stanford CRFM HELM team! CLEVA has now been integrated into the [latest release](https://github.com/stanford-crfm/helm/releases/tag/v0.3.0) of HELM. You can use CLEVA to evaluate your own models locally via HELM. 33 | - **\[2023.09.30\]** CLEVA has been accepted to [EMNLP 2023 System Demonstrations](https://2023.emnlp.org/calls/demos/)! 34 | - **\[2023.08.09\]** Our [paper](https://arxiv.org/abs/2308.04813) for CLEVA is out! 35 | 36 | 37 | ## 📌 Instructions 38 | 39 | [CLEVA](https://arxiv.org/abs/2308.04813) has been integrated into [HELM](https://github.com/stanford-crfm/helm). CLEVA would like to thank Stanford CRFM HELM team for the support. Users can employ CLEVA's datasets, prompt templates, perturbations, and Chinese automatic metrics for local evaluations via HELM. 40 | 41 | > [!NOTE] 42 | > If you want to evaluate your models on CLEVA online, please contact us via for authentication and check out [📘Documentation](http://www.lavicleva.com/#/homepage/createautotask) for API development. 43 | 44 | ### 🛠️ Installation 45 | 46 | Users can refer to the [installation guide](https://crfm-helm.readthedocs.io/en/latest/installation/) of HELM for setting up the Python environment and dependencies (`Python>=3.8`). 47 | 48 |
49 | Installation Using Anaconda 50 | 51 | Here is an example for installation using [Anaconda](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html): 52 | 53 | Create the environment first: 54 | ```sh 55 | # Create virtual environment 56 | # Only need to run once 57 | conda create -n cleva python=3.8 pip 58 | 59 | # Activate the virtual environment 60 | conda activate cleva 61 | ``` 62 | 63 | Then install the dependencies: 64 | 65 | ```sh 66 | pip install crfm-helm 67 | ``` 68 |
69 | 70 | ### ⚖️ Evaluation 71 | 72 | Example command to evaluate `gpt-3.5-turbo-0613` on CLEVA's Chinese-to-English translation task using HELM: 73 | 74 | ```sh 75 | helm-run \ 76 | -r "cleva:model=openai/gpt-3.5-turbo-0613,task=translation,subtask=zh2en,prompt_id=0,version=v1,data_augmentation=cleva" \ 77 | --num-train-trials \ 78 | --max-eval-instances \ 79 | --suite 80 | ``` 81 | 82 | Explanation of parameters in `-r` (run configuration): 83 | 84 | - `task` represents one of the 31 tasks included in CLEVA; 85 | - `subtask` specifies the subcategory under each CLEVA task; 86 | - `prompt_id` is the index of CLEVA's annotated prompt templates (starting from 0); 87 | - `version` is the version number of the CLEVA dataset (currently only the `v1` dataset used in the paper is provided); 88 | - `data_augmentation` specifies the data augmentation strategy, where values like `cleva_robustness`, `cleva_fairness`, and `cleva` are unique to CLEVA for evaluating Chinese language robustness, fairness and both respectively. 89 | 90 | For other parameters, please refer to HELM's [tutorial](https://crfm-helm.readthedocs.io/en/latest/tutorial/). 91 | 92 | The full list of available `task`, `subtask`, and `prompt_id` of CLEVA (`version=v1`) can be found in HELM's [.conf](https://github.com/stanford-crfm/helm/blob/main/src/helm/benchmark/presentation/run_specs_cleva_v1.conf) file. Users can run the entire CLEVA evaluation suite using the following command (the running time for reproducing CLEVA results can be found in the [paper](https://arxiv.org/abs/2308.04813)): 93 | 94 | ```sh 95 | helm-run \ 96 | -c src/helm/benchmark/presentation/run_specs_cleva_v1.conf \ 97 | --num-train-trials \ 98 | --max-eval-instances \ 99 | --suite 100 | ``` 101 | Generally, setting `--max-eval-instances` to over 5000 ensures all CLEVA task data are used for evaluation. 102 | 103 | ### 📊 Reference Results 104 | 105 | Comparison between the results obtained using HELM for evaluating `gpt-3.5-turbo-0613` on selected CLEVA tasks (`version=v1`) and those from the CLEVA platform: 106 | 107 | | Scenario | Metric | Reproduced in HELM | Evaluated by CLEVA | 108 | | ---- | ----------------- | ---------------- | ----------- | 109 | | task=summarization,subtask=dialogue_summarization | ROUGE-2 | 0.3045 | 0.3065 | 110 | | task=translation,subtask=en2zh | SacreBLEU | 60.48 | 59.23 | 111 | | task=fact_checking | Exact Match | 0.4595 | 0.4528 | 112 | | task=bias,subtask=dialogue_region_bias | Micro F1 | 0.5656 | 0.5589 | 113 | 114 | > [!NOTE] 115 | > The difference is mainly due to different random seeds resulting in different in-context demonstrations, and the ChatGPT versions used by CLEVA and HELM are not completely aligned. 116 | 117 | ## ⏬ Download Data 118 | 119 | If you want to use CLEVA data for evaluation with your own code, you can download the data by: 120 | ```sh 121 | bash download_data.sh 122 | ``` 123 | After a successful run, a folder named with the data version will be generated in the current directory, which contains the data of each task of CLEVA. You can specify the data version by passing arguments to `download_data.sh`. It is `v1` by default. 124 | 125 | ## 🛂 License 126 | 127 | CLEVA is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. 128 | 129 | You should have received a copy of the license along with this work. If not, see . 130 | 131 | ## 🖊️ Citation 132 | 133 | Please cite our paper if you use CLEVA in your work: 134 | ```bib 135 | @misc{li2024c2leva, 136 | title={C$^2$LEVA: Toward Comprehensive and Contamination-Free Language Model Evaluation}, 137 | author={Yanyang Li and Tin Long Wong and Cheung To Hung and Jianqiao Zhao and Duo Zheng and Ka Wai Liu and Michael R. Lyu and Liwei Wang}, 138 | year={2024}, 139 | eprint={2412.04947}, 140 | archivePrefix={arXiv}, 141 | primaryClass={cs.CL}, 142 | url={https://arxiv.org/abs/2412.04947}, 143 | } 144 | 145 | @misc{li2023cleva, 146 | title={CLEVA: Chinese Language Models EVAluation Platform}, 147 | author={Yanyang Li and Jianqiao Zhao and Duo Zheng and Zi-Yuan Hu and Zhi Chen and Xiaohui Su and Yongfeng Huang and Shijia Huang and Dahua Lin and Michael R. Lyu and Liwei Wang}, 148 | year={2023}, 149 | eprint={2308.04813}, 150 | archivePrefix={arXiv}, 151 | primaryClass={cs.CL} 152 | } 153 | ``` 154 | -------------------------------------------------------------------------------- /README_zh-CN.md: -------------------------------------------------------------------------------- 1 | #

CLEVA: Chinese Language Models EVAluation Platform

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3 | 4 | [![GitHub Repo stars](https://img.shields.io/github/stars/Lavi-Lab/CLEVA)](https://github.com/Lavi-Lab/CLEVA/stargazers) 5 | [![License: CC BY-NC-ND 4.0](https://img.shields.io/badge/License-CC_BY--NC--ND_4.0-blue.svg)](https://creativecommons.org/licenses/by-nc-nd/4.0/) 6 | [![Ask me anything](https://img.shields.io/badge/Ask%20me-anything-blue.svg)](https://github.com/LaVi-Lab/CLEVA/issues/new) 7 | 8 | [🌐网站](http://www.lavicleva.com/) 9 | •[📜论文 \[EMNLP 2023 Demo\]](https://arxiv.org/abs/2308.04813) 10 | •[📌教程](#instructions) 11 | •✉️邮箱 12 | 13 | [English](README.md) | 简体中文 14 | 15 |
16 | 17 | ## 🎯 引言 18 | 19 | CLEVA的主要亮点包括 20 | - 一个全面的**中文评测基准**，包括31个任务（11个应用评估+20个能力评测任务），共370K中文测试样本（33.98%为全新构造，*缓解数据泄露*的问题）； 21 | - 一个标准的**中文评测流程**，其中包括所有数据进行统一的预处理，所有模型使用同样*一组中文提示模板*进行评测等。 22 | - 一个可信的**中文模型排行榜**，因为CLEVA使用了大量新数据来缓解数据泄露，并定期组织评测。 23 | 24 | 其中排行榜由CLEVA使用全新的测试数据进行评测和维护。往期排行榜使用到的评测数据（处理好的测试样本、标注的提示模板等）将开放给用户，用户可自行在本地运行评测任务使用。 25 | 26 | ![总览](overview.png) 27 | 28 | ## 🔥 新闻 29 | 30 | - **\[2023.11.02\]** 感谢斯坦福CRFM HELM团队的支持！CLEVA现在已经被整合到HELM的[最新版本](https://github.com/stanford-crfm/helm/releases/tag/v0.3.0)。用户可以通过HELM来使用CLEVA的数据进行线下评测了。 31 | - **\[2023.09.30\]** CLEVA被[EMNLP 2023 System Demonstrations](https://2023.emnlp.org/calls/demos/)接收！ 32 | - **\[2023.08.09\]** CLEVA的[论文](https://arxiv.org/abs/2308.04813)在ArXiv放出！ 33 | 34 | 35 | ## 📌 教程 36 | 37 | [CLEVA](https://arxiv.org/abs/2308.04813)目前已经整合到[HELM](https://github.com/stanford-crfm/helm)中。CLEVA感谢斯坦福CRFM HELM团队的支持。用户可以通过HELM来使用CLEVA提供的数据集、标注提示模板、数据增强策略和中文自动指标来进行本地的评测。 38 | 39 | > **注意**
40 | > 如果您想使用CLEVA线上评测您的模型，请通过获取授权并查阅[📘文档](http://www.lavicleva.com/#/homepage/createautotask)进行API开发. 41 | 42 | ### 🛠️ 安装 43 | 44 | 用户可以参考HELM的[安装教程](https://crfm-helm.readthedocs.io/en/latest/installation/)进行Python环境以及依赖项的安装（`Python>=3.8`）。 45 | 46 |
47 | 通过Anaconda进行安装 48 | 49 | 其中使用[Anaconda](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)进行安装的例子如下： 50 | 51 | 首先创建环境： 52 | ```sh 53 | # 创建虚拟环境 54 | # 只需要运行一次 55 | conda create -n cleva python=3.8 pip 56 | 57 | # 激活虚拟环境 58 | conda activate cleva 59 | ``` 60 | 61 | 然后安装依赖项： 62 | ```sh 63 | pip install crfm-helm 64 | ``` 65 |
66 | 67 | ### ⚖️ 评测 68 | 69 | 使用HELM评测`gpt-3.5-turbo-0613`在CLEVA中到英翻译任务的结果例子如下： 70 | ```sh 71 | helm-run \ 72 | -r "cleva:model=openai/gpt-3.5-turbo-0613,task=translation,subtask=zh2en,prompt_id=0,version=v1,data_augmentation=cleva" \ 73 | --num-train-trials \ 74 | --max-eval-instances \ 75 | --suite 76 | ``` 77 | 其中`-r`是运行配置，里面每项参数解释如下： 78 | - `task`是CLEVA囊括的31个任务； 79 | - `subtask`是每个CLEVA任务下更细分的子任务类别； 80 | - `prompt_id`是CLEVA提供的标注提示模板索引（从0开始）； 81 | - `version`是CLEVA数据集版本号（目前仅提供论文使用的`v1`版本数据集）； 82 | - `data_augmentaion`是数据增强策略，其中取值`cleva_robustness`（评价中文健壮性的数据增强策略），`cleva_fairness`（评价中文公平性的数据增强策略）和`cleva`（同时评价中文健壮性和公平性）是CLEVA特有的中文数据增强策略。 83 | 84 | 其他参数的含义参考HELM的[教程](https://crfm-helm.readthedocs.io/en/latest/tutorial/)。 85 | 86 | CLEVA数据集（`version=v1`）对应的所有可用`task`，`subtask`和`prompt_id`取值参考HELM里的[配置文件](https://github.com/stanford-crfm/helm/blob/main/src/helm/benchmark/presentation/run_specs_cleva_v1.conf)。用户可以通过以下命令来运行整个CLEVA的评测（复现CLEVA结果的运行时间请参考[论文](https://arxiv.org/abs/2308.04813)）： 87 | ```sh 88 | helm-run \ 89 | -c src/helm/benchmark/presentation/run_specs_cleva_v1.conf \ 90 | --num-train-trials \ 91 | --max-eval-instances \ 92 | --suite 93 | ``` 94 | 一般而言，`--max-eval-instances`大于5000可以保证使用了CLEVA每个任务上的所有数据进行评测。 95 | 96 | ### 📊 参考结果 97 | 98 | 使用HELM评测`gpt-3.5-turbo-0613`在部分CLEVA任务（`version=v1`）上的结果与CLEVA平台运行的结果对比： 99 | 100 | | 任务 | 指标 | HELM复现结果 | CLEVA评测结果 | 101 | | ---- | ----------------- | ---------------- | ----------- | 102 | | task=summarization,subtask=dialogue_summarization | ROUGE-2 | 0.3045 | 0.3065 | 103 | | task=translation,subtask=en2zh | SacreBLEU | 60.48 | 59.23 | 104 | | task=fact_checking | Exact Match | 0.4595 | 0.4528 | 105 | | task=bias,subtask=dialogue_region_bias | Micro F1 | 0.5656 | 0.5589 | 106 | 107 | > **注意**
108 | > 结果的差异主要是由于随机种子不同导致不同的in-context示例以及CLEVA和HELM使用的ChatGPT版本不完全对齐。 109 | 110 | ## ⏬ 数据下载 111 | 112 | 如果您想在自己的代码中使用CLEVA的数据进行评测，您可以通过以下命令下载数据： 113 | ```sh 114 | bash download_data.sh 115 | ``` 116 | 运行成功后会在当前目录生成以数据版本命名的文件夹，里面包含CLEVA各个任务的数据。您可以通过向`download_data.sh`传递参数来指定数据版本。默认是`v1`。 117 | 118 | ## 🛂 许可 119 | 120 | CLEVA is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. 121 | 122 | You should have received a copy of the license along with this work. If not, see . 123 | 124 | ## 🖊️ 引用 125 | 126 | 如果您在您的工作中使用了CLEVA，请引用我们的论文： 127 | ```bib 128 | @misc{li2023cleva, 129 | title={CLEVA: Chinese Language Models EVAluation Platform}, 130 | author={Yanyang Li and Jianqiao Zhao and Duo Zheng and Zi-Yuan Hu and Zhi Chen and Xiaohui Su and Yongfeng Huang and Shijia Huang and Dahua Lin and Michael R. Lyu and Liwei Wang}, 131 | year={2023}, 132 | eprint={2308.04813}, 133 | archivePrefix={arXiv}, 134 | primaryClass={cs.CL} 135 | } 136 | ``` 137 | -------------------------------------------------------------------------------- /download_data.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | set -e 3 | 4 | version=${1:-v1} 5 | 6 | wget "http://39.108.215.175/data/${version}.zip" 7 | mkdir -p ${version} 8 | unzip ${version}.zip -d ${version} 9 | rm -rf ${version}.zip 10 | -------------------------------------------------------------------------------- /overview.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LaVi-Lab/CLEVA/9b11c68e6321db0aad5d3c571094451884180c78/overview.png --------------------------------------------------------------------------------