mlsanityData to 14 Oct 2026

NormTab: Improving Symbolic Reasoning in LLMs Through Tabular Data Normalization

arXiv 2406.17961 ↗First posted 25 Jun 2024cs.CL cs.AI cs.DBcited by 16

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

Abstract (arXiv)

In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in parsing textual data and generating code. However, their performance in tasks involving tabular data, especially those requiring symbolic reasoning, faces challenges due to the structural variance and inconsistency in table cell values often found in web tables. In this paper, we introduce NormTab, a novel framework aimed at enhancing the symbolic reasoning performance of LLMs by normalizing web tables. We study table normalization as a stand-alone, one-time preprocessing step using LLMs to support symbolic reasoning on tabular data. Our experimental evaluation, conducted on challenging web table datasets such as WikiTableQuestion and TabFact, demonstrates that leveraging NormTab significantly improves symbolic reasoning performance, showcasing the importance and effectiveness of web table normalization for enhancing LLM-based symbolic reasoning tasks.

Key facts. Cited by 16 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 18 of its 35 references that are in the map. First posted on arXiv 25 Jun 2024.

Builds on

18 of 35 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition15 Apr
  2. 2024Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding9 Jan
  3. 2023Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning31 Jan
  4. 2022Large Language Models are few(1)-shot Table Reasoners13 Oct

Other references · 14

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  3. 2015Compositional Semantic Parsing on Semi-Structured Tablescited by 515
  4. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 477
  5. 2020TAPAS: Weakly Supervised Table Parsing via Pre-trainingcited by 434
  6. 2019TabFact: A Large-scale Dataset for Table-based Fact Verificationcited by 428
  7. 2023StructGPT: A General Framework for Large Language Model to Reason over Structured Datacited by 343
  8. 2023LEVER: Learning to Verify Language-to-Code Generation with Executioncited by 239
  9. 2022Binding Language Models in Symbolic Languagescited by 228
  10. 2022Can Foundation Models Wrangle Your Data?cited by 175
  11. 2023TableLlama: Towards Open Large Generalist Models for Tablescited by 137
  12. 2023ReAcTable: Enhancing ReAct for Table Question Answeringcited by 76
2 more
  1. 2022PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-trainingcited by 41
  2. 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: 2024: 3; 2025: 11; 2026: 2.

16 papers.

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  1. 2024Interpretable LLM-based Table Question Answeringcited by 18
  2. 2025TableMaster: A Recipe to Advance Table Understanding with Language Modelscited by 18
  3. 2025When TableQA Meets Noise: A Dual Denoising Framework for Complex Questions and Large-scale Tablescited by 10
  4. 2024FLEXTAF: Enhancing Table Reasoning with Flexible Tabular Formatscited by 8
  5. 2025Weaver: Interweaving SQL and LLM for Table Reasoningcited by 8
  6. 2025Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluationcited by 5
  7. 2025Rethinking Schema Linking: A Context-Aware Bidirectional Retrieval Approach for Text-to-SQLcited by 4
  8. 2024TabulaX: Leveraging Large Language Models for Multi-Class Table Transformationscited by 3
  9. 2025Tab-MIA: A Benchmark Dataset for Membership Inference Attacks on Tabular Data in LLMscited by 3
  10. 2025M3TQA: Massively Multilingual Multitask Table Question Answeringcited by 3
  11. 2025AraTable: Benchmarking LLMs' Reasoning and Understanding of Arabic Tabular Datacited by 2
  12. 2025Mind the (Language) Gap: Towards Probing Numerical and Cross-Lingual Limits of LVLMscited by 2
4 more
  1. 2025Database Normalization via Dual-LLM Self-Refinementcited by 1
  2. 2025RUST-BENCH: Benchmarking LLM Reasoning on Unstructured Text within Structured Tablescited by 1
  3. 2026QUIETT: Query-Independent Table Transformation for Robust Reasoningcited by 0
  4. 2026Can LLMs Normalize Databases? A Benchmark and Multi-Agent Framework for Schema Normalizationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Rethinking Tabular Data Understanding with Large Language Models
  2. 2026ProgramTab: Boosting Table Reasoning of LLMs via Programmatic Paradigm
  3. 2025RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis
  4. 2025Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges
  5. 2024Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding
  6. 2024OpenTab: Advancing Large Language Models as Open-domain Table Reasoners
  7. 2025TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models
  8. 2024A Survey of Table Reasoning with Large Language Models