mlsanityData to 14 Oct 2026

TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT

arXiv 2307.08674 ↗First posted 17 Jul 2023cs.AI cs.LGcited by 59

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Cited by 26 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Tables are prevalent in real-world databases, requiring significant time and effort for humans to analyze and manipulate. The advancements in large language models (LLMs) have made it possible to interact with tables using natural language input, bringing this capability closer to reality. In this paper, we present TableGPT, a unified fine-tuned framework that enables LLMs to understand and operate on tables using external functional commands. It introduces the capability to seamlessly interact with tables, enabling a wide range of functionalities such as question answering, data manipulation (e.g., insert, delete, query, and modify operations), data visualization, analysis report generation, and automated prediction. TableGPT aims to provide convenience and accessibility to users by empowering them to effortlessly leverage tabular data. At the core of TableGPT lies the novel concept of global tabular representations, which empowers LLMs to gain a comprehensive understanding of the entire table beyond meta-information. By jointly training LLMs on both table and text modalities, TableGPT achieves a deep understanding of tabular data and the ability to perform complex operations on tables through chain-of-command instructions. Importantly, TableGPT offers the advantage of being a self-contained system rather than relying on external API interfaces. Moreover, it supports efficient data process flow, query rejection (when appropriate) and private deployment, enabling faster domain data fine-tuning and ensuring data privacy, which enhances the framework's adaptability to specific use cases.

Key facts. Cited by 59 papers in the map (data through 14 Oct 2026). Builds on 27 of its 39 references that are in the map. First posted on arXiv 17 Jul 2023.

Builds on

27 of 39 references.

References · 27

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  4. 2020Language Models are Few-Shot Learnerscited by 32,419
  5. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  6. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  7. 2023Segment Anythingcited by 8,234
  8. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  9. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  10. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  11. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
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15 more
  1. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 687
  2. 2020A Survey of Deep Active Learningcited by 554
  3. 2017Tacotron: Towards End-to-End Speech Synthesiscited by 490
  4. 2022TabLLM: Few-shot Classification of Tabular Data with Large Language Modelscited by 277
  5. 2023AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Headcited by 265
  6. 2023MultiModal-GPT: A Vision and Language Model for Dialogue with Humanscited by 255
  7. 2023MathPrompter: Mathematical Reasoning using Large Language Modelscited by 236
  8. 2023ChatDB: Augmenting LLMs with Databases as Their Symbolic Memorycited by 140
  9. 2023Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsingcited by 93
  10. 2023Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflowcited by 76
  11. 2023Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuningcited by 58
  12. 2023SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Modelscited by 57
  13. 2023Phoenix: Democratizing ChatGPT across Languagescited by 32
  14. 2023Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibilitycited by 30
  15. 2023Towards Cross-Table Masked Pretraining for Web Data Miningcited by 21

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 8; 2024: 17; 2025: 22; 2026: 12.

59 papers.

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  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  2. 2024Tulu 3: Pushing Frontiers in Open Language Model Post-Trainingcited by 1,053
  3. 2025Multimodal Chain-of-Thought Reasoning: A Comprehensive Surveycited by 182
  4. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 159
  5. 2024ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoningcited by 128
  6. 2024TableBench: A Comprehensive and Complex Benchmark for Table Question Answeringcited by 128
  7. 2023Can Large Language Models Understand Real-World Complex Instructions?cited by 69
  8. 2024Making Pre-trained Language Models Great on Tabular Predictioncited by 57
  9. 2023TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usagecited by 55
  10. 2023Tabular Representation, Noisy Operators, and Impacts on Table Structure Understanding Tasks in LLMscited by 49
  11. 2024Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMscited by 48
  12. 2024Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Modelscited by 44
38 more
  1. 2023OceanGPT: A Large Language Model for Ocean Science Taskscited by 43
  2. 2024Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMscited by 40
  3. 2023TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systemscited by 39
  4. 2025Large-Scale Data Selection for Instruction Tuningcited by 35
  5. 2024CadVLM: Bridging Language and Vision in the Generation of Parametric CAD Sketchescited by 28
  6. 2025Table-R1: Inference-Time Scaling for Table Reasoningcited by 24
  7. 2025TableMind: An Autonomous Programmatic Agent for Tool-Augmented Table Reasoningcited by 20
  8. 2025Reasoning-Table: Exploring Reinforcement Learning for Table Reasoningcited by 18
  9. 2024SheetAgent: Towards A Generalist Agent for Spreadsheet Reasoning and Manipulation via Large Language Modelscited by 17
  10. 2024TQA-Bench: Evaluating LLMs for Multi-Table Question Answeringcited by 17
  11. 2024TrustUQA: A Trustful Framework for Unified Structured Data Question Answeringcited by 12
  12. 2024cPAPERS: A Dataset of Situated and Multimodal Interactive Conversations in Scientific Paperscited by 11
  13. 2025AgriGPT: a Large Language Model Ecosystem for Agriculturecited by 11
  14. 2025Rethinking Table Instruction Tuningcited by 10
  15. 2025Sparks of Tabular Reasoning via Text2SQL Reinforcement Learningcited by 9
  16. 2024AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systemscited by 7
  17. 2026MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillationcited by 7
  18. 2025StruPhantom: Evolutionary Injection Attacks on Black-Box Tabular Agents Powered by Large Language Modelscited by 6
  19. 2026From Rows to Reasoning: A Retrieval-Augmented Multimodal Framework for Spreadsheet Understandingcited by 6
  20. 2025TabularMath: Understanding Math Reasoning over Tables with Large Language Modelscited by 5
  21. 2023HeLM: Highlighted Evidence augmented Language Model for Enhanced Table-to-Text Generationcited by 4
  22. 2024V-RECS, a Low-Cost LLM4VIS Recommender with Explanations, Captioning and Suggestionscited by 4
  23. 2026TABQAWORLD: Optimizing Multimodal Reasoning for Multi-Turn Table Question Answeringcited by 4
  24. 2023RDBench: ML Benchmark for Relational Databasescited by 3
  25. 2024Using Large Language Models to Generate Clinical Trial Tables and Figurescited by 3
  26. 2025Formula-R1: Incentivizing LLM Reasoning over Complex Tables with Numerical Computation via Formula-Driven Reinforcement Learningcited by 3
  27. 2024TIMeSynC: Temporal Intent Modelling with Synchronized Context Encodings for Financial Service Applicationscited by 2
  28. 2025MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learningcited by 2
  29. 2025TableReasoner: Advancing Table Reasoning Framework with Large Language Modelscited by 2
  30. 2025Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligencecited by 2
  31. 2025Utilizing Training Data to Improve LLM Reasoning for Tabular Understandingcited by 2
  32. 2025Strengthening LLMs for Tabular Prediction with Structural Priorscited by 2
  33. 2025Self-Correction Distillation for Structured Data Question Answeringcited by 2
  34. 2025Tabular Embeddings for Tables with Bi-Dimensional Hierarchical Metadata and Nestingcited by 1
  35. 2025Enabling Few-Shot Alzheimer's Disease Diagnosis on Biomarker Data with Tabular LLMscited by 1
  36. 2026ReasonTabQA: A Comprehensive Benchmark for Table Question Answering from Real World Industrial Scenarioscited by 1
  37. 2026TableMind++: An Uncertainty-Aware Programmatic Agent for Tool-Augmented Table Reasoningcited by 1
  38. 2026Enhancing LLM Metacognition via Cognitive Pairwise Trainingcited by 1

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