mlsanityData to 14 Oct 2026

Large Language Models are few(1)-shot Table Reasoners

arXiv 2210.06710 ↗First posted 13 Oct 2022cs.CLcited by 116

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

Abstract (arXiv)

Recent literature has shown that large language models (LLMs) are generally excellent few-shot reasoners to solve text reasoning tasks. However, the capability of LLMs on table reasoning tasks is yet to be explored. In this paper, we aim at understanding how well LLMs can perform table-related tasks with few-shot in-context learning. Specifically, we evaluated LLMs on popular table QA and fact verification datasets like WikiTableQuestion, FetaQA, TabFact, and FEVEROUS and found that LLMs are competent at complex reasoning over table structures, though these models are not pre-trained on any table corpus. When combined with `chain of thoughts' prompting, LLMs can achieve very strong performance with only a 1-shot demonstration, even on par with some SoTA models. We show that LLMs are even more competent at generating comprehensive long-form answers on FetaQA than tuned T5-large. We further manually studied the reasoning chains elicited from LLMs and found that these reasoning chains are highly consistent with the underlying semantic form. We believe that LLMs can serve as a simple yet generic baseline for future research. The code and data are released in https://github.com/wenhuchen/TableCoT.

Key facts. Cited by 116 papers in the map (data through 14 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 43 of its 48 references that are in the map. First posted on arXiv 13 Oct 2022.

Builds on

43 of 48 references.

References · 43

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  5. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  6. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  7. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  8. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  9. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  10. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,470
  11. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,407
  12. 2021Scaling Language Models: Methods, Analysis & Insights from Training Gophercited by 1,202
31 more
  1. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  2. 2021GLaM: Efficient Scaling of Language Models with Mixture-of-Expertscited by 897
  3. 2018MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modellingcited by 766
  4. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 687
  5. 2021FinQA: A Dataset of Numerical Reasoning over Financial Datacited by 562
  6. 2022Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Modelcited by 559
  7. 2015Compositional Semantic Parsing on Semi-Structured Tablescited by 515
  8. 2020TAPAS: Weakly Supervised Table Parsing via Pre-trainingcited by 434
  9. 2019TabFact: A Large-scale Dataset for Table-based Fact Verificationcited by 428
  10. 2021TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Financecited by 408
  11. 2020TaBERT: Pretraining for Joint Understanding of Textual and Tabular Datacited by 398
  12. 2020ToTTo: A Controlled Table-To-Text Generation Datasetcited by 268
  13. 2020HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Datacited by 264
  14. 2021TAPEX: Table Pre-training via Learning a Neural SQL Executorcited by 235
  15. 2022UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Modelscited by 231
  16. 2022Binding Language Models in Symbolic Languagescited by 228
  17. 2021MultiModalQA: Complex Question Answering over Text, Tables and Imagescited by 179
  18. 2021FeTaQA: Free-form Table Question Answeringcited by 175
  19. 2020GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsingcited by 171
  20. 2020Open Question Answering over Tables and Textcited by 163
  21. 2020DART: Open-Domain Structured Data Record to Text Generationcited by 145
  22. 2022Compositional Semantic Parsing with Large Language Modelscited by 95
  23. 2020Logical Natural Language Generation from Open-Domain Tablescited by 90
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  27. 2020Program Enhanced Fact Verification with Verbalization and Graph Attention Networkcited by 29
  28. 2022HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Datacited by 28
  29. 2019Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programscited by 26
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 5; 2023: 32; 2024: 48; 2025: 22; 2026: 9.

116 papers.

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  1. 2022Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Themcited by 1,807
  2. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,076
  3. 2022Towards Reasoning in Large Language Models: A Surveycited by 596
  4. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 477
  5. 2023StructGPT: A General Framework for Large Language Model to Reason over Structured Datacited by 343
  6. 2022Reasoning with Language Model Prompting: A Surveycited by 252
  7. 2024Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understandingcited by 188
  8. 2023Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Futurecited by 184
  9. 2023Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoningcited by 178
  10. 2022DePlot: One-shot visual language reasoning by plot-to-table translationcited by 160
  11. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 159
  12. 2023Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Studycited by 116
38 more
  1. 2024Large Language Models Meet NLP: A Surveycited by 103
  2. 2023Towards Better Chain-of-Thought Prompting Strategies: A Surveycited by 62
  3. 2023Tabular Representation, Noisy Operators, and Impacts on Table Structure Understanding Tasks in LLMscited by 49
  4. 2023Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategiescited by 45
  5. 2023DocMath-Eval: Evaluating Math Reasoning Capabilities of LLMs in Understanding Long and Specialized Documentscited by 45
  6. 2024Multimodal Table Understandingcited by 44
  7. 2023RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbationscited by 43
  8. 2024TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decompositioncited by 39
  9. 2024FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answeringcited by 39
  10. 2023Rethinking Tabular Data Understanding with Large Language Modelscited by 36
  11. 2023TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoningcited by 32
  12. 2023Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Promptingcited by 31
  13. 2024A Survey of Table Reasoning with Large Language Modelscited by 29
  14. 2023Large Language Models are Complex Table Parserscited by 28
  15. 2024Evaluating LLMs' Mathematical Reasoning in Financial Document Question Answeringcited by 25
  16. 2024OpenTab: Advancing Large Language Models as Open-domain Table Reasonerscited by 25
  17. 2024Interactive-KBQA: Multi-Turn Interactions for Knowledge Base Question Answering with Large Language Modelscited by 25
  18. 2025Large Language Models are Good Relational Learnerscited by 24
  19. 2023Effective Distillation of Table-based Reasoning Ability from LLMscited by 23
  20. 2024CABINET: Content Relevance based Noise Reduction for Table Question Answeringcited by 23
  21. 2024ProTrix: Building Models for Planning and Reasoning over Tables with Sentence Contextcited by 23
  22. 2023Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuningcited by 21
  23. 2024UQE: A Query Engine for Unstructured Databasescited by 21
  24. 2023Is Bigger and Deeper Always Better? Probing LLaMA Across Scales and Layerscited by 20
  25. 2024Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understandingcited by 20
  26. 2024TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Datacited by 19
  27. 2024FinDVer: Explainable Claim Verification over Long and Hybrid-Content Financial Documentscited by 19
  28. 2023MMHQA-ICL: Multimodal In-context Learning for Hybrid Question Answering over Text, Tables and Imagescited by 18
  29. 2024GraphOTTER: Evolving LLM-based Graph Reasoning for Complex Table Question Answeringcited by 18
  30. 2024Interpretable LLM-based Table Question Answeringcited by 18
  31. 2025TableMaster: A Recipe to Advance Table Understanding with Language Modelscited by 18
  32. 2024SheetAgent: Towards A Generalist Agent for Spreadsheet Reasoning and Manipulation via Large Language Modelscited by 17
  33. 2024NormTab: Improving Symbolic Reasoning in LLMs Through Tabular Data Normalizationcited by 16
  34. 2023From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuningcited by 14
  35. 2023Leveraging LLMs in Scholarly Knowledge Graph Question Answeringcited by 13
  36. 2023Investigating Table-to-Text Generation Capabilities of LLMs in Real-World Information Seeking Scenarioscited by 12
  37. 2023DOMINO: A Dual-System for Multi-step Visual Language Reasoningcited by 12
  38. 2024Efficient Prompting for LLM-based Generative Internet of Thingscited by 12

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  2. 2025How well do LLMs reason over tabular data, really?
  3. 2026Probing How Scalable Table Data Enhances General Long-Context Reasoning
  4. 2026ProgramTab: Boosting Table Reasoning of LLMs via Programmatic Paradigm
  5. 2025TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models
  6. 2025Advancing Reasoning in Large Language Models: Promising Methods and Approaches
  7. 2025Reasoning Capabilities and Invariability of Large Language Models
  8. 2024Lucy: Think and Reason to Solve Text-to-SQL