mlsanityData to 7 Oct 2026

Tab-CoT: Zero-shot Tabular Chain of Thought

arXiv 2305.17812 ↗First posted 28 May 2023cs.CLcited by 42

On the map: LLM reasoning → Prompting and chain-of-thought reasoning

Abstract (arXiv)

The chain-of-though (CoT) prompting methods were successful in various natural language processing (NLP) tasks thanks to their ability to unveil the underlying complex reasoning processes. Such reasoning processes typically exhibit implicitly structured steps. Recent efforts also started investigating methods to encourage more explicitly structured reasoning procedures to be captured. In this work, we propose Tab-CoT, a novel tabular-format CoT prompting method, which allows the complex reasoning process to be explicitly modelled in a highly structured manner. Despite its simplicity, we show that our approach is capable of performing reasoning across multiple dimensions (i.e., both rows and columns). We demonstrate our approach's strong zero-shot and few-shot capabilities through extensive experiments on a range of reasoning tasks.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 14 of its 18 references that are in the map. First posted on arXiv 28 May 2023.

Builds on

14 of 18 references.

Closest ancestors · 4

Same direction, newest first

  1. 2022Large Language Models are Zero-Shot Reasoners24 May
  2. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Models21 May
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Models21 Mar
  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Models28 Jan

Other references · 10

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  4. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,793
  5. 2021Are NLP Models really able to Solve Simple Math Word Problems?cited by 861
  6. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 736
  7. 2017Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problemscited by 642
  8. 2022PAL: Program-aided Language Modelscited by 635
  9. 2016Solving General Arithmetic Word Problemscited by 353
  10. 2022Rethinking with Retrieval: Faithful Large Language Model Inferencecited by 145

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 15; 2025: 11; 2026: 9.

42 papers.

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  1. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 188
  2. 2024Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understandingcited by 185
  3. 2023Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Futurecited by 184
  4. 2024LLM Multi-Agent Systems: Challenges and Open Problemscited by 119
  5. 2023Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answeringcited by 97
  6. 2023Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agentscited by 72
  7. 2024GroupDebate: Enhancing the Efficiency of Multi-Agent Debate Using Group Discussioncited by 64
  8. 2024Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMscited by 48
  9. 2023Measuring and Improving Chain-of-Thought Reasoning in Vision-Language Modelscited by 45
  10. 2025TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoningcited by 22
  11. 2025S2-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiencycited by 21
  12. 2024Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understandingcited by 20
30 more
  1. 2024TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Datacited by 19
  2. 2023Retrieval-augmented Multi-modal Chain-of-Thoughts Reasoning for Large Language Modelscited by 18
  3. 2025Exposing Numeracy Gaps: A Benchmark to Evaluate Fundamental Numerical Abilities in Large Language Modelscited by 14
  4. 2024GenDec: A robust generative Question-decomposition method for Multi-hop reasoningcited by 13
  5. 2024TART: An Open-Source Tool-Augmented Framework for Explainable Table-based Reasoningcited by 10
  6. 2026ReGuLaR: Variational Latent Reasoning Guided by Rendered Chain-of-Thoughtcited by 8
  7. 2026Multi-Agent Debate with Memory Maskingcited by 8
  8. 2023Exploring Equation as a Better Intermediate Meaning Representation for Numerical Reasoningcited by 7
  9. 2024DCR: Divide-and-Conquer Reasoning for Multi-choice Question Answering with LLMscited by 5
  10. 2024Nash CoT: Multi-Path Inference with Preference Equilibriumcited by 4
  11. 2025GateLens: A Reasoning-Enhanced LLM Agent for Automotive Software Release Analyticscited by 4
  12. 2024Performance Assessment of ChatGPT vs Bard in Detecting Alzheimer's Dementiacited by 3
  13. 2025Improving Table Understanding with LLMs and Entity-Oriented Searchcited by 3
  14. 2024Pyramid-Driven Alignment: Pyramid Principle Guided Integration of Large Language Models and Knowledge Graphscited by 2
  15. 2025AraTable: Benchmarking LLMs' Reasoning and Understanding of Arabic Tabular Datacited by 2
  16. 2025Predicting Language Models' Success at Zero-Shot Probabilistic Predictioncited by 2
  17. 2025Mitigating Visual Context Degradation in Large Multimodal Models: A Training-Free Decoupled Agentic Frameworkcited by 2
  18. 2026CFMS: A Coarse-to-Fine Multimodal Synthesis Framework for Enhanced Tabular Reasoningcited by 2
  19. 2024AdaCoder: Adaptive Prompt Compression for Programmatic Visual Question Answeringcited by 1
  20. 2024HySem: A context length optimized LLM pipeline for unstructured tabular extractioncited by 1
  21. 2024Talking with Tables for Better LLM Factual Data Interactionscited by 1
  22. 2025Can Structured Templates Facilitate LLMs in Tackling Harder Tasks? : An Exploration of Scaling Laws by Difficultycited by 1
  23. 2025More Bias, Less Bias: BiasPrompting for Enhanced Multiple-Choice Question Answeringcited by 1
  24. 2026TableMind++: An Uncertainty-Aware Programmatic Agent for Tool-Augmented Table Reasoningcited by 1
  25. 2026TabClaw: An Interactive and Self-Evolving Agent for Spreadsheet Manipulation and Table Reasoningcited by 1
  26. 2026A Technical Taxonomy of LLM Agent Communication Protocolscited by 1
  27. 2025Summarize-Exemplify-Reflect: Data-driven Insight Distillation Empowers LLMs for Few-shot Tabular Classificationcited by 0
  28. 2026Replacing Multi-Step Assembly of Data Preparation Pipelines with One-Step LLM Pipeline Generation for Table QAcited by 0
  29. 2026DataFactory: Collaborative Multi-Agent Framework for Advanced Table Question Answeringcited by 0
  30. 2026LLM-X: A Scalable Negotiation-Oriented Exchange for Communication Among Personal LLM Agentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
  2. 2023Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages
  3. 2024Break the Chain: Large Language Models Can be Shortcut Reasoners
  4. 2024Enhancing Chain of Thought Prompting in Large Language Models via Reasoning Patterns
  5. 2024Instance-adaptive Zero-shot Chain-of-Thought Prompting
  6. 2025Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding
  7. 2024Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Models
  8. 2022Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters