| Name | Tittle | Keywords | Artifact | Paper |
|---|---|---|---|---|
| Codex | Evaluating Large Language Models Trained on Code | Code Generation | - | arXiv 21 |
| GPT-3 | Language Models are Few-Shot Learners | LLM, in-context learning | - | NeurIPS 20 |
| BERT | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | Pre-training | Model | NAACL 19 |
| SpreadsheetCoder | SpreadsheetCoder: Formula Prediction from Semi-structured Context | Spreadsheet formula prediction | Code | ICML 21 |
| TURL | TURL: Table Understanding through Representation Learning | Table Pre-training | Code | VLDB 20 |
| TaPEx | TAPEX: Table Pre-training via Learning a Neural SQL Executor | Table Pre-training | Code | ICLR 22 |
| TaBERT | TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data | Table Pre-training | Code | ACL 20 |
| TaPas | TaPas: Weakly Supervised Table Parsing via Pre-training | Table Pre-training | Code | ACL 20 |
| TABBIE | TABBIE: Pretrained Representations of Tabular Data | Table Pre-training | Code | ACL 21 |
| RESDSQL | RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL | text-to-SQL fine-tuning | Code | AAAI 23 |
| FLAN | Finetuned Language Models Are Zero-Shot Learners | Instruction-tuning | Code | ICLR 22 |
| Chain-of-thought | Chain-of-thought prompting elicits reasoning in large language models | Step-by-step Reasoning | - | NeurIPS 22 |
| Least-to-most | Least-to-most prompting enables complex reasoning in large language models | Prompting, Decompose | - | ICLR 23 |
| Self-consistency | Self-consistency improves chain of thought reasoning in language models | Reason with votes | - | ICLR 23 |
| Table-GPT | Table-GPT: Table-tuned GPT for Diverse Table Tasks | Table Instruction-tuning | - | arXiv 23 |
| TableLlama | TableLlama: Towards Open Large Generalist Models for Tables | Table Instruction-tuning | Model | arXiv 23 |
| UnifiedSKG | UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models | Table Instruction-tuning | Code | EMNLP 22 |
| Code Llama | Code Llama: Open Foundation Models for Code | Code | Code | arXiv 23 |
| Magicoder | Magicoder: Source Code Is All You Need | Code Instruction-tuning | Code Model | arXiv 23 |
| Lemur | Lemur: Harmonizing Natural Language and Code for Language Agents | Code Instruction-tuning | Code Model | ICLR 24 |
| ReAct | ReAct: Synergizing Reasoning and Acting in Language Models | Agent Framework | Code | ICLR 23 |
| Dater | Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning | Prompting, Decomposing | Code | SIGIR 23 |
| StructGPT | StructGPT: A General Framework for Large Language Model to Reason over Structured Data | Prompting, Structured Knowledge | Code | EMNLP 23 |
| DAIL-SQL | Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation | Prompting, text-to-SQL | Code | arXiv 23 |
| C3 | C3: Zero-shot Text-to-SQL with ChatGPT | Prompting | Code | arXiv 23 |
| DIN-SQL | DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction | Prompting, Decompose | Code | NeurIPS 23 |
| Prompt Design Strategies | Enhancing Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies | Prompting, text-to-SQL | Code | EMNLP 23 |
| Binder | Binding Language Models in Symbolic Languages | Prompting | Code | ICLR 23 |
| Data-Copilot | Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow | Agent, Data Visualization | Code | arXiv 23 |
| SheetCopilot | SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models | Agent, Spreadsheet | Code | NeurIPS 23 |
| ReAcTable | ReAcTable: Enhancing ReAct for Table Question Answering | Agent, ReAct | Code | arXiv 23 |
| TAP4LLM | TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning | Table Augmentation | arXiv 23 | |
| DB-GPT | DB-GPT: Empowering Database Interactions with Private Large Language Models | Industry Framework | Code | arXiv 23 |
| DPR | Dense Passage Retrieval for Open-Domain Question Answering | Retrieval-Augmented Generation | Code | EMNLP 20 |
| ITR | An Inner Table Retriever for Robust Table Question Answering | Retrieval | Code | ACL 23 |
| LI-RAGE | LI-RAGE: Late Interaction Retrieval Augmented Generation with Explicit Signals for Open-Domain Table Question Answering | RAG + Table | Code | ACL 23 |
| Name | Keywords | Artifact | Paper |
|---|---|---|---|
| MBPP | Code | link | arXiv 21 |
| HumanEval | Code | link | arXiv 21 |
| Dr.Spider | Text-to-SQL, Robustness | link | ICLR 23 |
| TabFact | Table QA | link | ICLR 20 |
| HyBirdQA | Table QA | link | EMNLP 20 |
| AnaMeta | Table Metadata | link | ACL 23 |
| InfiAgent-DABench | Data Analysis | link | arXiv 24 |
| GitTables | GitHub CSVs | link | SIGMOD 23 |
| DS-1000 | Data Analysis | link | ICML 23 |
| WDC | Web Table | link | WWW 16 |
| BIRD | Text-to-SQL | link | NeurIPS 23 |
| DART | Table-to-text | link | NAACL 21 |
| FetaQA | Table QA | link | TACL 22 |
| ToTTo | Table-to-text | link | EMNLP 20 |
| WiKiTableQuestions | Table QA | link | ACL 15 |
| sheetperf | Spreadsheet | link | SIGMOD 20 |
| GPT4Table | Table QA, Table-to-text | link | WSDM 24 |
| Spider | Text-to-SQL | link | EMNLP 18 |
| RobuT | Table QA | link | ACL 23 |
| WiKiSQL | Table QA,Text-to-SQL | link | arXiv 17 |
| Observatory | Table Embedding Benchmark | Code | VLDB 24 |