mlsanityData to 7 Oct 2026

OpenTab: Advancing Large Language Models as Open-domain Table Reasoners

arXiv 2402.14361 ↗First posted 22 Feb 2024cs.LGcited by 25

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

Abstract (arXiv)

Large Language Models (LLMs) trained on large volumes of data excel at various natural language tasks, but they cannot handle tasks requiring knowledge that has not been trained on previously. One solution is to use a retriever that fetches relevant information to expand LLM's knowledge scope. However, existing textual-oriented retrieval-based LLMs are not ideal on structured table data due to diversified data modalities and large table sizes. In this work, we propose OpenTab, an open-domain table reasoning framework powered by LLMs. Overall, OpenTab leverages table retriever to fetch relevant tables and then generates SQL programs to parse the retrieved tables efficiently. Utilizing the intermediate data derived from the SQL executions, it conducts grounded inference to produce accurate response. Extensive experimental evaluation shows that OpenTab significantly outperforms baselines in both open- and closed-domain settings, achieving up to 21.5% higher accuracy. We further run ablation studies to validate the efficacy of our proposed designs of the system.

Key facts. Cited by 25 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 31 of its 35 references that are in the map. First posted on arXiv 22 Feb 2024.

Builds on

31 of 35 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. 2022Large Language Models are few(1)-shot Table Reasoners13 Oct
  3. 2022TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data25 May

Other references · 28

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  5. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  6. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  7. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  8. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  9. 2021Improving language models by retrieving from trillions of tokenscited by 1,278
  10. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
  11. 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
  12. 2022Towards Reasoning in Large Language Models: A Surveycited by 593
16 more
  1. 2015Compositional Semantic Parsing on Semi-Structured Tablescited by 509
  2. 2020TAPAS: Weakly Supervised Table Parsing via Pre-trainingcited by 432
  3. 2021TAPEX: Table Pre-training via Learning a Neural SQL Executorcited by 233
  4. 2022UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Modelscited by 231
  5. 2022Binding Language Models in Symbolic Languagescited by 224
  6. 2022The Unreliability of Explanations in Few-shot Prompting for Textual Reasoningcited by 169
  7. 2021FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured informationcited by 166
  8. 2020Open Question Answering over Tables and Textcited by 163
  9. 2022Rethinking with Retrieval: Faithful Large Language Model Inferencecited by 145
  10. 2023A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capabilitycited by 118
  11. 2021Open Domain Question Answering over Tables via Dense Retrievalcited by 74
  12. 2023Open-WikiTable: Dataset for Open Domain Question Answering with Complex Reasoning over Tablecited by 40
  13. 2022OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answeringcited by 29
  14. 2022A Thorough Examination on Zero-shot Dense Retrievalcited by 21
  15. 2020Joint Verification and Reranking for Open Fact Checking Over Tablescited by 16
  16. 2022Table Retrieval May Not Necessitate Table-specific Model Designcited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 10; 2025: 11; 2026: 4.

25 papers.

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  1. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 157
  2. 2024Why Tabular Foundation Models Should Be a Research Prioritycited by 103
  3. 2024Large Language Models Meet NLP: A Surveycited by 102
  4. 2024UDA: A Benchmark Suite for Retrieval Augmented Generation in Real-world Document Analysiscited by 40
  5. 2024Interpretable LLM-based Table Question Answeringcited by 18
  6. 2025RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarkingcited by 17
  7. 2025HyperG: Hypergraph-Enhanced LLMs for Structured Knowledgecited by 11
  8. 2024Matchmaker: Self-Improving Large Language Model Programs for Schema Matchingcited by 9
  9. 2024UniHGKR: Unified Instruction-aware Heterogeneous Knowledge Retrieverscited by 5
  10. 2025Chain-of-Query: Unleashing the Power of LLMs in SQL-Aided Table Understanding via Multi-Agent Collaborationcited by 4
  11. 2025Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluationcited by 4
  12. 2024TabVer: Tabular Fact Verification with Natural Logiccited by 3
13 more
  1. 2024RETQA: A Large-Scale Open-Domain Tabular Question Answering Dataset for Real Estate Sectorcited by 3
  2. 2025Improving Table Understanding with LLMs and Entity-Oriented Searchcited by 3
  3. 2025TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answeringcited by 3
  4. 2025HyFedRAG: A Federated Retrieval-Augmented Generation Framework for Heterogeneous and Privacy-Sensitive Datacited by 3
  5. 2025Are We Asking the Right Questions? On Ambiguity in Natural Language Queries for Tabular Data Analysiscited by 3
  6. 2024Evaluating LLMs on Entity Disambiguation in Tablescited by 2
  7. 2025Towards Question Answering over Large Semi-structured Tablescited by 2
  8. 2025Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligencecited by 2
  9. 2025Planning for Success: Exploring LLM Long-term Planning Capabilities in Table Understandingcited by 2
  10. 2026ODUTQA-MDC: A Task for Open-Domain Underspecified Tabular QA with Multi-turn Dialogue-based Clarificationcited by 1
  11. 2026Query Symbolically or Retrieve Semantically? A Dataset and Method for Semi-Structured Question Answeringcited by 1
  12. 2026A Multi-Agent Approach for Claim Verification from Tabular Data Documentscited by 0
  13. 2026Retrieve Only Relevant Tables Whether Few or Many: Adaptive Table Retrieval Methodcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026ProgramTab: Boosting Table Reasoning of LLMs via Programmatic Paradigm
  2. 2025TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models
  3. 2023TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning
  4. 2024TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition
  5. 2025Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges
  6. 2024Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding
  7. 2023Rethinking Tabular Data Understanding with Large Language Models
  8. 2024NormTab: Improving Symbolic Reasoning in LLMs Through Tabular Data Normalization