├── .gitignore └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | # idea 2 | .idea/ -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # LLM-Table-Survey 2 | ## Table of Contents 3 | 4 | - [LLM-Table-Survey](#llm-table-survey) 5 | - [Table of Contents](#table-of-contents) 6 | - [📄 Paper List](#-paper-list) 7 | - [Large Language Model](#large-language-model) 8 | - [Pre-LLM Era Table Training](#pre-llm-era-table-training) 9 | - [Table Instruction-Tuning](#table-instruction-tuning) 10 | - [Code LLM](#code-llm) 11 | - [Hybrid of Table \& Code](#hybrid-of-table--code) 12 | - [Multimodal Table Understanding \& Extraction](#multimodal-table-understanding--extraction) 13 | - [Representation](#representation) 14 | - [Prompting](#prompting) 15 | - [NL2SQL](#nl2sql) 16 | - [Table QA](#table-qa) 17 | - [Spreadsheet](#spreadsheet) 18 | - [Multi-task Framework](#multi-task-framework) 19 | - [Tools](#tools) 20 | - [Survey](#survey) 21 | - [📊 Datasets \& Benchmarks](#-datasets--benchmarks) 22 | - [Benchmarks](#benchmarks) 23 | - [Datasets](#datasets) 24 | 25 | ## 📄 Paper List 26 | 27 | ### Large Language Model 28 | 29 | * GPT-3, Language Models are Few-Shot Learners. NeurIPS 20. \[[Paper](https://papers.nips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html)\] 30 | * T5, Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. \[[Paper](https://jmlr.org/papers/v21/20-074.html)\] 31 | * FLAN, Finetuned Language Models Are Zero-Shot Learners. ICLR 22. \[[Paper](https://openreview.net/pdf?id=gEZrGCozdqR)\] \[[Code](https://github.com/google-research/FLAN)\] 32 | * DPO, Direct Preference Optimization: Your Language Model is Secretly a Reward Model. NeurIPS 23. \[[Paper](https://arxiv.org/pdf/2305.18290)\] 33 | * PEFT, The Power of Scale for Parameter-Efficient Prompt Tuning. EMNLP 21. \[[Paper](https://aclanthology.org/2021.emnlp-main.243.pdf)\] 34 | * LoRA, LoRA: Low-rank Adaptation of Large Language Models. ICLR 22. \[[Paper](https://arxiv.org/pdf/2106.09685)\] 35 | * Chain-of-thought Prompting, Chain-of-thought prompting elicits reasoning in large language models. NeurIPS 22. \[[Paper](https://proceedings.neurips.cc/paper_files/paper/2022/hash/9d5609613524ecf4f15af0f7b31abca4-Abstract-Conference.html)\] 36 | * Least-to-most Prompting, Least-to-most prompting enables complex reasoning in large language models. ICLR 23. \[[Paper](https://openreview.net/pdf?id=WZH7099tgfM)\] 37 | * Self-consistency Prompting, Self-consistency improves chain of thought reasoning in language models. ICLR 23. \[[Paper](https://openreview.net/pdf?id=1PL1NIMMrw)\] 38 | * ReAct, ReAct: Synergizing Reasoning and Acting in Language Models. ICLR 23. \[[Paper](https://openreview.net/forum?id=WE_vluYUL-X)\] \[[Code](https://github.com/ysymyth/ReAct)\] 39 | 40 | ### Pre-LLM Era Table Training 41 | 42 | * TaBERT, TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data. ACL 20 Main. \[[Paper](https://aclanthology.org/2020.acl-main.745/)\] \[[Code](https://github.com/facebookresearch/TaBERT)\] 43 | * TaPEx, TAPEX: Table Pre-training via Learning a Neural SQL Executor. ICLR 22. \[[Paper](https://openreview.net/pdf?id=O50443AsCP)\] \[[Code](https://github.com/microsoft/Table-Pretraining)\] \[[Models](https://huggingface.co/models?search=microsoft/tapex)\] 44 | * TABBIE, TABBIE: Pretrained Representations of Tabular Data. NAACL 21 Main. \[[Paper](https://aclanthology.org/2021.naacl-main.270/)\] \[[Code](https://github.com/SFIG611/tabbie)\] 45 | * TURL, TURL: Table Understanding through Representation Learning. VLDB 21. \[[Paper](https://www.vldb.org/pvldb/vol14/p307-deng.pdf)\] \[[Code](https://github.com/sunlab-osu/TURL)\] 46 | * RESDSQL, RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL. AAAI 23. \[[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/26535/26307)\] \[[Code](https://github.com/RUCKBReasoning/RESDSQL)\] 47 | * UnifiedSKG, UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models. EMNLP 22 Main. \[[Paper](https://aclanthology.org/2022.emnlp-main.39/) \] \[[Code](https://github.com/xlang-ai/UnifiedSKG)\] 48 | * SpreadsheetCoder, SpreadsheetCoder: Formula Prediction from Semi-structured Context. ICML 21. \[[Paper](https://arxiv.org/abs/2106.15339)\] \[[Code](https://github.com/google-research/google-research/tree/master/spreadsheet_coder)\] 49 | 50 | 51 | ### Table Instruction-Tuning 52 | 53 | * Table-GPT, Table-GPT: Table-tuned GPT for Diverse Table Tasks. arXiv 2023. \[[Paper](https://arxiv.org/abs/2310.09263)\] 54 | * TableLlama, TableLlama: Towards Open Large Generalist Models for Tables. NAACL 24. \[[Paper](https://arxiv.org/abs/2311.09206)\] \[[Code](https://github.com/OSU-NLP-Group/TableLlama)\] \[[Model: TableLlama 7B](https://huggingface.co/osunlp/TableLlama)\] \[[Dataset: TableInstruct](https://huggingface.co/datasets/osunlp/TableInstruct)\] 55 | 56 | ### Code LLM 57 | 58 | * Codex, Evaluating Large Language Models Trained on Code. arXiv 21. \[[Paper](https://arxiv.org/abs/2107.03374)\] 59 | * StarCoder, StarCoder: may the source be with you!. TMLR 23. \[[Paper](https://arxiv.org/abs/2305.06161)\] \[[Code](https://github.com/bigcode-project/starcoder)\] \[[Models](https://huggingface.co/bigcode/starcoder)\] 60 | * Code Llama, Code Llama: Open Foundation Models for Code. arXiv 23. \[[Paper](https://arxiv.org/abs/2308.12950)\] \[[Code](https://github.com/meta-llama/codellama)\] 61 | * WizardLM, WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions. ICLR 24. \[[Paper](https://openreview.net/forum?id=CfXh93NDgH)\] \[[Model: WizardLM 13B](https://huggingface.co/WizardLMTeam/WizardLM-13B-V1.0)\] \[[Model: WizardLM 70B](https://huggingface.co/WizardLMTeam/WizardLM-70B-V1.0)\] 62 | * WizardCoder, WizardCoder: Empowering Code Large Language Models with Evol-Instruct. ICLR 24. \[[Paper](https://openreview.net/forum?id=UnUwSIgK5W)\] \[[Code](https://github.com/nlpxucan/WizardLM)\] \[[Models: WizardCoder 15B](https://huggingface.co/WizardLMTeam/WizardCoder-15B-V1.0)\] 63 | * Magicoder, Magicoder: Source Code Is All You Need. ICML 24. \[[Paper](https://arxiv.org/abs/2312.02120)\] \[[Code](https://github.com/ise-uiuc/magicoder)\] \[[Models 6.7B/7B](https://huggingface.co/models?search=ise-uiuc/Magicoder)\] 64 | * Lemur, Lemur: Harmonizing Natural Language and Code for Language Agents. ICLR 24. \[[Paper](https://openreview.net/forum?id=hNhwSmtXRh)\] \[[Code](https://github.com/OpenLemur/Lemur)\] \[[Model: Lemur 70B](https://huggingface.co/OpenLemur/lemur-70b-v1)\] \[[Model: Lemur 70B Chat](https://huggingface.co/OpenLemur/lemur-70b-chat-v11)\] 65 | * InfiAgent-DABench, InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks. ICML 24. \[[Paper](https://arxiv.org/abs/2401.05507)\] \[[Code](https://github.com/InfiAgent/InfiAgent)\] 66 | 67 | ### Hybrid of Table & Code 68 | * TableLLM, TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios. \[[Paper](http://arxiv.org/abs/2403.19318)\] \[[Model TableLLM 7B](https://huggingface.co/RUCKBReasoning/TableLLM-7b)\] \[[Model TableLLM 13B](https://huggingface.co/RUCKBReasoning/TableLLM-13b)\] 69 | * StructLM, StructLM: Towards Building Generalist Models for Structured Knowledge Grounding. arXiv 24. \[[Paper](https://arxiv.org/abs/2402.16671)\] \[[Model: StructLM 7B](https://huggingface.co/TIGER-Lab/StructLM-7B)\] \[[Model: StructLM 13B](https://huggingface.co/TIGER-Lab/StructLM-13B)\] \[[Model: StructLM 34B](https://huggingface.co/TIGER-Lab/StructLM-34B)\] \[[Dataset: SKGInstruct](https://huggingface.co/datasets/TIGER-Lab/SKGInstruct)\] 70 | 71 | ### Parameter-Efficient Fine-Tuning 72 | 73 | * FinSQL, FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial Analysis. SIGMOD Companion 24. [\[Paper\](https://arxiv.org/pdf/2401.10506)] 74 | 75 | ### Direct Preference Optimization 76 | 77 | * SENSE, Synthesizing Text-to-SQL Data from Weak and Strong LLMs. ACL 24. \[[Paper](https://aclanthology.org/2024.acl-long.425.pdf)\] 78 | 79 | 80 | ### Small Language Model + Large Language Model 81 | 82 | * ZeroNL2SQL, Combining Small Language Models and Large Language Models for Zero-Shot NL2SQL. VLDB 24. \[[Paper](https://dl.acm.org/doi/10.14778/3681954.3681960)\] 83 | 84 | ### Multimodal Table Understanding & Extraction 85 | 86 | * LayoutLM, LayoutLM: Pre-training of Text and Layout for Document Image Understanding. KDD 20. \[[Paper](https://dl.acm.org/doi/10.1145/3394486.3403172)\] 87 | * PubTabNet, Image-Based Table Recognition: Data, Model, and Evaluation. ECCV 20. \[[Paper](https://link.springer.com/chapter/10.1007/978-3-030-58589-1_34)\] \[[Code & Data](https://github.com/ibm-aur-nlp/PubTabNet)\] 88 | * Table-LLaVA, Multimodal Table Understanding. ACL 24. \[[Paper](https://arxiv.org/abs/2406.08100)\] \[[Code](https://github.com/SpursGoZmy/Table-LLaVA)\] \[[Model](https://huggingface.co/SpursgoZmy/table-llava-v1.5-7b)\] 89 | * TableLVM, TableVLM: Multi-modal Pre-training for Table Structure Recognition. ACL 23. \[[Paper](https://aclanthology.org/2023.acl-long.137/)\] 90 | * PixT3, PixT3: Pixel-based Table-To-Text Generation. ACL 24. \[[Paper](https://aclanthology.org/2024.acl-long.364.pdf)\] 91 | 92 | ### Representation 93 | 94 | * Tabular representation, noisy operators, and impacts on table structure understanding tasks in LLMs. NeurIPS 2023 second table representation learning workshop. \[[Paper](https://openreview.net/forum?id=Ld5UCpiT07)\] 95 | * SpreadsheetLLM, SpreadsheetLLM: Encoding Spreadsheets for Large Language Models. arXiv 24. \[[Paper](http://arxiv.org/abs/2407.09025)\] 96 | * Enhancing Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies. EMNLP 23. \[[Paper](https://aclanthology.org/2023.findings-emnlp.996.pdf)\] \[[Code](https://github.com/linyongnan/STRIKE)\] 97 | * Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs. arXiv 24. \[[Paper](http://arxiv.org/abs/2402.12424)\] 98 | 99 | ### Prompting 100 | #### NL2SQL 101 | 102 | * The Dawn of Natural Language to SQL: Are We Fully Ready? VLDB 24. \[[Paper](http://arxiv.org/abs/2406.01265)\] \[[Code](https://github.com/HKUSTDial/NL2SQL360)\] 103 | * MCS-SQL, MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation. \[[Paper](http://arxiv.org/abs/2405.07467)\] 104 | * DIN-SQL, DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction Prompting, Decompose. NeurIPS 23. \[[Paper](https://proceedings.neurips.cc/paper_files/paper/2023/hash/72223cc66f63ca1aa59edaec1b3670e6-Abstract-Conference.html)\] \[[Code](https://github.com/MohammadrezaPourreza/Few-shot-NL2SQL-with-prompting)\] 105 | * DAIL-SQL, Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation. VLDB 24. \[[Paper](https://arxiv.org/abs/2308.15363)\] \[[Code](https://github.com/BeachWang/DAIL-SQL)\] 106 | * C3, C3: Zero-shot Text-to-SQL with ChatGPT. arXiv 24. \[[Paper](https://arxiv.org/abs/2307.07306)\] \[[Code](https://github.com/bigbigwatermalon/C3SQL)\] 107 | 108 | #### Table QA 109 | 110 | * Dater, Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning. SIGIR 23. \[[Paper](https://arxiv.org/abs/2301.13808)\] \[[Code](https://github.com/AlibabaResearch/DAMO-ConvAI)\] 111 | * Binder, Binding language models in symbolic languages. ICLR 23. \[[Paper](https://arxiv.org/abs/2210.02875)\] \[[Code](https://github.com/xlang-ai/Binder)\] 112 | * ReAcTable, ReAcTable: Enhancing ReAct for Table Question Answering. VLDB 24. \[[Paper](https://arxiv.org/abs/2310.00815)\] \[[Code](https://github.com/yunjiazhang/ReAcTable)\] 113 | * E5, E5: Zero-shot Hierarchical Table Analysis using Augmented LLMs via Explain, Extract, Execute, Exhibit and Extrapolate. NAACL 24. \[[Paper](https://aclanthology.org/2024.naacl-long.68/)\] \[[Code](https://github.com/zzh-SJTU/E5-Hierarchical-Table-Analysis)\] 114 | * Chain-of-Table, Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding. ICLR 24. \[[Paper](https://arxiv.org/abs/2401.04398)\] 115 | * ITR, An Inner Table Retriever for Robust Table Question Answering. ACL 23. \[[Paper](https://aclanthology.org/2023.acl-long.551)\] 116 | * LI-RAGE, LI-RAGE: Late Interaction Retrieval Augmented Generation with Explicit Signals for Open-Domain Table Question Answering. ACL 23. \[[Paper](https://aclanthology.org/2023.acl-short.133)\] 117 | 118 | #### Spreadsheet 119 | 120 | * SheetCopilot, SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models Agent. NeurIPS 23. \[[Paper](https://arxiv.org/abs/2305.19308)\] \[[Code](https://sheetcopilot.github.io/)\] 121 | * SheetAgent, SheetAgent: A Generalist Agent for Spreadsheet Reasoning and Manipulation via Large Language Models. arXiv 24. \[[Paper](http://arxiv.org/abs/2403.03636)\] 122 | * Vision Language Models for Spreadsheet Understanding: Challenges and Opportunities. arXiv 24. \[[Paper](http://arxiv.org/abs/2405.16234)\] 123 | 124 | #### Multi-task Framework 125 | 126 | * StructGPT, StructGPT: A General Framework for Large Language Model to Reason over Structured Data. EMNLP 23 Main. \[[Paper](https://aclanthology.org/2023.emnlp-main.574/)\] \[[Code](https://github.com/RUCAIBox/StructGPT)\] 127 | * TAP4LLM, TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning. arXiv 23. \[[Paper](https://arxiv.org/abs/2312.09039)\] 128 | * UniDM, UniDM: A Unified Framework for Data Manipulation with Large Language Models. MLSys 24. \[[Paper](https://arxiv.org/abs/2405.06510)\] 129 | * Data-Copilot, Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow. arXiv 23. \[[Paper](https://arxiv.org/abs/2306.07209)\] \[[Code](https://github.com/zwq2018/Data-Copilot)\] 130 | 131 | ### Tools 132 | 133 | * [LlamaIndex](https://github.com/run-llama/llama_index) 134 | * [PandasAI](https://github.com/sinaptik-ai/pandas-ai) 135 | * [Vanna](https://github.com/vanna-ai/vanna) 136 | * DB-GPT. DB-GPT: Empowering Database Interactions with Private Large Language Models. \[[Paper](http://arxiv.org/abs/2312.17449)\] \[[Code](https://github.com/eosphoros-ai/DB-GPT)\] 137 | * RetClean. RetClean: Retrieval-Based Data Cleaning Using Foundation Models and Data Lakes. \[[Paper](http://arxiv.org/abs/2303.16909)\] \[[Code](https://github.com/qcri/RetClean)\] 138 | 139 | 140 | ### Survey 141 | 142 | * A Survey of Large Language Models. \[[Paper](http://arxiv.org/abs/2303.18223)\] 143 | * A Survey on Large Language Model Based Autonomous Agents. \[[Paper](https://link.springer.com/10.1007/s11704-024-40231-1)\] 144 | * Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks. \[[Paper](https://www.ijcai.org/proceedings/2022/761)\] 145 | * Transformers for tabular data representation: A survey of models and applications. \[[Paper](https://aclanthology.org/2023.tacl-1.14)\] 146 | * A Survey of Table Reasoning with 147 | Large Language Models. \[[Paper](http://arxiv.org/abs/2402.08259)\] 148 | * A survey on table question answering: Recent advances. \[[Paper](https://doi.org/10.1007/978-981-19-7596-7_14)\] 149 | * Large Language Models(LLMs) on Tabular Data - A Survey. \[[Paper](http://arxiv.org/abs/2402.17944)\] 150 | * A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions. \[[Paper](http://arxiv.org/abs/2208.13629)\] 151 | 152 | ## 📊 Datasets & Benchmarks 153 | 154 | ### Benchmarks 155 | 156 | | Name | Keywords | Artifact | Paper | 157 | |--------------------|---------------------------|--------------------------------------------------------------------------|-------------------------------------------------------------| 158 | | MBPP | Code | [link](https://huggingface.co/datasets/mbpp) | [arXiv 21](https://arxiv.org/abs/2108.07732) | 159 | | HumanEval | Code | [link](https://github.com/openai/human-eval) | [arXiv 21](https://arxiv.org/abs/2107.03374) | 160 | | Dr.Spider | NL2SQL, Robustness | [link](https://github.com/awslabs/diagnostic-robustness-text-to-sql) | [ICLR 23](https://arxiv.org/abs/2301.08881) | 161 | | WiKiTableQuestions | Table QA | [link](https://github.com/ppasupat/WikiTableQuestions) | [ACL 15](https://aclanthology.org/P15-1142/) | 162 | | WiKiSQL | Table QA,NL2SQL | [link](https://github.com/salesforce/WikiSQL) | [arXiv 17](https://arxiv.org/abs/1709.00103) | 163 | | TabFact | Table Fact Verification | [link](https://tabfact.github.io/) | [ICLR 20](https://arxiv.org/abs/1909.02164) | 164 | | HyBirdQA | Table QA | [link](https://github.com/wenhuchen/HybridQA) | [EMNLP 20](https://arxiv.org/abs/2004.07347) | 165 | | FetaQA | Table Fact Verification | [link](https://github.com/Yale-LILY/FeTaQA) | [TACL 22](https://aclanthology.org/2022.tacl-1.3/) | 166 | | RobuT | Table QA | [link](https://github.com/yilunzhao/RobuT) | [ACL 23](https://arxiv.org/abs/2306.14321) | 167 | | AnaMeta | Table Metadata | [link](https://github.com/microsoft/AnaMeta) | [ACL 23](https://arxiv.org/abs/2209.00946) | 168 | | GPT4Table | Table QA, Table-to-text | [link](https://github.com/Y-Sui/GPT4Table) | [WSDM 24](https://arxiv.org/abs/2305.13062) | 169 | | ToTTo | Table-to-text | [link](https://github.com/google-research-datasets/totto) | [EMNLP 20](https://aclanthology.org/2020.emnlp-main.89/) | 170 | | SpreadsheetBench | Spreadsheet Manipulation | [link](https://github.com/RUCKBReasoning/SpreadsheetBench) | [NeurIPS 24](https://arxiv.org/abs/2406.14991) | 171 | | BIRD | NL2SQL | [link](https://bird-bench.github.io/) | [NeurIPS 23](https://arxiv.org/abs/2305.03111) | 172 | | Spider | NL2SQL | [link](https://yale-lily.github.io/spider) | [EMNLP 18](https://arxiv.org/abs/1809.08887) | 173 | | Dr.Spider | NL2SQL | [link](https://github.com/awslabs/diagnostic-robustness-text-to-sql) | [ICLR 23](https://arxiv.org/abs/2301.08881) | 174 | | ScienceBenchmark | NL2SQL | [link](https://sciencebenchmark.cloudlab.zhaw.ch/) | [VLDB 24](https://arxiv.org/pdf/2306.04743) | 175 | | DS-1000 | Data Analysis | [link](https://ds1000-code-gen.github.io/) | [ICML 23](https://arxiv.org/abs/2211.11501) | 176 | | InfiAgent-DABench | Data Analysis | [link](https://github.com/InfiAgent/InfiAgent) | [ICML 24](https://arxiv.org/abs/2401.05507) | 177 | | TableBank | Table Detection | [link](https://doc-analysis.github.io/tablebank-page/) | [LERC 20](https://aclanthology.org/2020.lrec-1.236/) | 178 | | PubTabNet | Table Extraction | [link](https://github.com/ibm-aur-nlp/PubTabNet) | [ECCV 20](https://arxiv.org/abs/1911.10683) | 179 | | ComTQA | Visual Table QA, Table Detection, Table Extraction | [link](https://huggingface.co/datasets/ByteDance/ComTQA) | [arXiv 24](https://arxiv.org/abs/2406.01326v1) | 180 | 181 | 182 | ### Datasets 183 | 184 | 185 | | Name | Keywords | Artifact | Paper | 186 | |--------------------|---------------------------|--------------------------------------------------------------------------|-------------------------------------------------------------| 187 | | TableInstruct | Table Instruction Tuning | [link](https://huggingface.co/datasets/osunlp/TableInstruct) | [arXiv 23](https://arxiv.org/pdf/2311.09206.pdf) | 188 | | WDC | Web Table | [link](https://webdatacommons.org/) | [WWW 16](https://dl.acm.org/doi/10.1145/2872518.2889386) | 189 | | GitTables | GitHub CSVs | [link](https://gittables.github.io/) | [SIGMOD 23](https://arxiv.org/abs/2106.07258) | 190 | | DART | Table-to-text | [link](https://github.com/Yale-LILY/dart) | [NAACL 21](https://aclanthology.org/2021.naacl-main.37/) | 191 | | MMTab | Multimodal Table Understanding | [link](https://huggingface.co/datasets/SpursgoZmy/MMTab) | [ACL 24](https://arxiv.org/abs/2406.08100) | 192 | | SchemaPile | Database Schemas | [link](https://schemapile.github.io/) | [SIGMOD 24](https://dl.acm.org/doi/abs/10.1145/3654975) | --------------------------------------------------------------------------------