mlsanityData to 7 Oct 2026

Rethinking Tabular Data Understanding with Large Language Models

arXiv 2312.16702 ↗First posted 27 Dec 2023cs.CL cs.AI cs.DBcited by 34

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

Abstract (arXiv)

Large Language Models (LLMs) have shown to be capable of various tasks, yet their capability in interpreting and reasoning over tabular data remains an underexplored area. In this context, this study investigates from three core perspectives: the robustness of LLMs to structural perturbations in tables, the comparative analysis of textual and symbolic reasoning on tables, and the potential of boosting model performance through the aggregation of multiple reasoning pathways. We discover that structural variance of tables presenting the same content reveals a notable performance decline, particularly in symbolic reasoning tasks. This prompts the proposal of a method for table structure normalization. Moreover, textual reasoning slightly edges out symbolic reasoning, and a detailed error analysis reveals that each exhibits different strengths depending on the specific tasks. Notably, the aggregation of textual and symbolic reasoning pathways, bolstered by a mix self-consistency mechanism, resulted in achieving SOTA performance, with an accuracy of 73.6% on WIKITABLEQUESTIONS, representing a substantial advancement over previous existing table processing paradigms of LLMs.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 30 of its 58 references that are in the map. First posted on arXiv 27 Dec 2023.

Builds on

30 of 58 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning31 Jan
  2. 2022ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples22 Oct
  3. 2022Large Language Models are few(1)-shot Table Reasoners13 Oct

Other references · 27

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  5. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  6. 2017Deep reinforcement learning from human preferencescited by 3,846
  7. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  8. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  9. 2020Learning to summarize from human feedbackcited by 2,562
  10. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,397
  11. 2022Scaling Laws for Reward Model Overoptimizationcited by 1,056
  12. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 820
15 more
  1. 2015Compositional Semantic Parsing on Semi-Structured Tablescited by 509
  2. 2020TAPAS: Weakly Supervised Table Parsing via Pre-trainingcited by 432
  3. 2020TaBERT: Pretraining for Joint Understanding of Textual and Tabular Datacited by 395
  4. 2023ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddingscited by 238
  5. 2021TAPEX: Table Pre-training via Learning a Neural SQL Executorcited by 233
  6. 2022UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Modelscited by 231
  7. 2022Making Large Language Models Better Reasoners with Step-Aware Verifiercited by 231
  8. 2022Binding Language Models in Symbolic Languagescited by 224
  9. 2020Open Question Answering over Tables and Textcited by 163
  10. 2023TableLlama: Towards Open Large Generalist Models for Tablescited by 136
  11. 2023Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflowcited by 76
  12. 2022PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-trainingcited by 41
  13. 2023RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbationscited by 41
  14. 2022Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learningcited by 32
  15. 2021Table-based Fact Verification with Salience-aware Learningcited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 10; 2025: 16; 2026: 8.

34 papers.

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  1. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 157
  2. 2024TableRAG: Million-Token Table Understanding with Language Modelscited by 40
  3. 2024A Survey of Table Reasoning with Large Language Modelscited by 29
  4. 2024Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environmentscited by 27
  5. 2024H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tablescited by 21
  6. 2025Exploring LLM Agents for Cleaning Tabular Machine Learning Datasetscited by 20
  7. 2025TableMaster: A Recipe to Advance Table Understanding with Language Modelscited by 18
  8. 2024Mitigating Backdoor Threats to Large Language Models: Advancement and Challengescited by 14
  9. 2024TART: An Open-Source Tool-Augmented Framework for Explainable Table-based Reasoningcited by 10
  10. 2024Exploring the Robustness of Language Models for Tabular Question Answering via Attention Analysiscited by 9
  11. 2024ALTER: Augmentation for Large-Table-Based Reasoningcited by 9
  12. 2025LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusioncited by 8
22 more
  1. 2025Table-r1: Self-supervised and Reinforcement Learning for Program-based Table Reasoning in Small Language Modelscited by 7
  2. 2025Efficient numeracy in language models through single-token number embeddingscited by 6
  3. 2025HIPPO: Enhancing the Table Understanding Capability of LLMs through Hybrid-Modal Preference Optimizationcited by 5
  4. 2025AutoPK: Leveraging LLMs and a Hybrid Similarity Metric for Advanced Retrieval of Pharmacokinetic Data from Complex Tables and Documentscited by 4
  5. 2024Reasoning Factual Knowledge in Structured Data with Large Language Modelscited by 3
  6. 2025Formula-R1: Incentivizing LLM Reasoning over Complex Tables with Numerical Computation via Formula-Driven Reinforcement Learningcited by 3
  7. 2025Improving Table Understanding with LLMs and Entity-Oriented Searchcited by 3
  8. 2025Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligencecited by 2
  9. 2025AraTable: Benchmarking LLMs' Reasoning and Understanding of Arabic Tabular Datacited by 2
  10. 2025Utilizing Training Data to Improve LLM Reasoning for Tabular Understandingcited by 2
  11. 2025RUST-BENCH: Benchmarking LLM Reasoning on Unstructured Text within Structured Tablescited by 1
  12. 2026Orthogonal Hierarchical Decomposition for Structure-Aware Table Understanding with Large Language Modelscited by 1
  13. 2025MRT at IberLEF-2025 PRESTA Task: Maximizing Recovery from Tables with Multiple Stepscited by 0
  14. 2025Instruction Tuning of Large Language Models for Tabular Data Generation-in One Daycited by 0
  15. 2025Debugging Tabular Log as Dynamic Graphscited by 0
  16. 2026What Language Models Know But Don't Say: Non-Generative Prior Extraction for Generalizationcited by 0
  17. 2026Reasoning by Commented Code for Table Question Answeringcited by 0
  18. 2026How Do Language Models Understand Tables? A Mechanistic Analysis of Cell Locationcited by 0
  19. 2026ASTRA: Adaptive Semantic Tree Reasoning Architecture for Complex Table Question Answeringcited by 0
  20. 2026The Power of Order: Fooling LLMs with Adversarial Table Permutationscited by 0
  21. 2026Semantically Similar, Logically Distinct: Diagnosing the Semantic-Answerability Gap in Table RAGcited by 0
  22. 2026SleuthBench: Benchmarking Statistical LLM Evaluation Using Tabular Hidden Signalscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges
  2. 2024Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding
  3. 2024NormTab: Improving Symbolic Reasoning in LLMs Through Tabular Data Normalization
  4. 2025How well do LLMs reason over tabular data, really?
  5. 2025TabularMath: Understanding Math Reasoning over Tables with Large Language Models
  6. 2026Probing How Scalable Table Data Enhances General Long-Context Reasoning
  7. 2026Enhancing Table Reasoning with Deterministic Table-State Rewards
  8. 2025RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis