A comprehensive paper list of Large Language Model for Table Understanding

🌟 We will be presenting a tutorial on “Large Language Models for Tabular Data” at the upcoming SIGIR’24 conference. See you in D.C. !😊

• Tutorial
• Dataset
• Paper
  • Survey
  • Table Encoding
  • Model Structure for Tabular Data
  • LLM Training with Tabular Data
  • LLM-driven Table Agents

• Fuse: a reproducible, extendable, internet-scale corpus of spreadsheets. [Paper] [Dataset]
• ToTTo: A Controlled Table-To-Text Generation Dataset. [Paper] [Dataset]

• Table pre-training: A survey on model architectures, pre-training objectives, and downstream tasks. [Paper]
• Large Language Model for Table Processing: A Survey. [Paper]
• A Survey of Table Reasoning with Large Language Models. [Paper]
• Large Language Models (LLMs) on Tabular Data: Prediction, Generation, and Understanding - A Survey [Paper]

• Tabular Representation, Noisy Operators, and Impacts on Table Structure Understanding Tasks in LLMs. [Paper]
• GPT4Table: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study. [Paper]
• DBCopilot: Scaling Natural Language Querying to Massive Databases. [Paper]
• Table-to-text generation by structure-aware seq2seq learning. [Paper]
• Data-to-text generation with content selection and planning. [Paper]
• Tables as Images? Exploring the Strengths and Limitations of LLMs on Multimodal Representations of Tabular Data. [Paper]
• Assessing GPT4-V on Structured Reasoning Tasks. [Paper]
• Multimodal Table Understanding. [Paper]
• TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergy. [Paper]
• Vision Language Models for Spreadsheet Understanding: Challenges and Opportunities. [Paper]
• Large Language Models are few(1)-shot Table Reasoners. [Paper]
• TaPas: Weakly Supervised Table Parsing via Pre-training. [Paper]
• TAPEX: Table Pre-training via Learning a Neural SQL Executor. [Paper]
• An inner table retriever for robust table question answering. [Paper]
• LI-RAGE: Late interaction retrieval augmented generation with explicit signals for open-domain table question answering. [Paper]
• DB-GPT: Empowering Database Interactions with Private Large Language Models. [Paper]

• TaPas: Weakly Supervised Table Parsing via Pre-training. [Paper]
• TURL: table understanding through representation learning. [Paper]
• TABBIE: Pretrained Representations of Tabular Data. [Paper]
• StructGPT: A General Framework for Large Language Model to Reason over Structured Data. [Paper]
• TAPEX: Table Pre-Training via Learning a Neural SQL Executor. [Paper]
• Tablegpt: Table-tuned gpt for diverse table tasks. [Paper]
• TableLlama: Towards Open Large Generalist Models for Tables. [Paper]
• TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios. [Paper]
• Tables as Images? Exploring the Strengths and Limitations of LLMs on Multimodal Representations of Tabular Data. [Paper]
• My Database User Is a Large Language Model. [Paper]
• LLM-Enhanced Data Management. [Paper]
• DB-GPT: Empowering Database Interactions with Private Large Language Models. [Paper]

• Table Retrieval May Not Necessitate Table-specific Model Design. [Paper]
• CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets. [Paper]
• Self-refine: Iterative refinement with self-feedback. [Paper]
• DIN-SQL: Decomposed InContext Learning of Text-to-SQL with Self-Correction. [Paper]
• Chain-of-table: Evolving tables in the reasoning chain for table understanding. [Paper]
• Large Language Models Are Versatile Decomposers: Decomposing Evidence and Questions for Table-based Reasoning. [Paper]
• ReAct: Synergizing Reasoning and Acting in Language Models. [Paper]
• StructGPT: A General Framework for Large Language Model to Reason over Structured Data. [Paper]
• TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning. [Paper]
• Self-Consistency Improves Chain of Thought Reasoning in Language Models. [Paper]
• SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models. [Paper]
• ReAcTable: Enhancing ReAct for Table Question Answering. [Paper]
• Binding Language Models in Symbolic Languages. [Paper]
• Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow. [Paper]
• TroVE: Inducing Verifiable and Efficient Toolboxes for Solving Programmatic Tasks. [Paper]