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A Survey on Table Question Answering: Recent Advances

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arxiv 2207.05270 v1 pith:OTTRGBFL submitted 2022-07-12 cs.CL cs.AI

Nengzheng Jin , Joanna Siebert , Dongfang Li , Qingcai Chen This is my paper · ORCID
classification cs.CLcs.AI
keywords tablemethodsquestionansweringexistingrecentthereaccording
verification ladder T0 review T1 audit T2 compute T3 formal
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Table Question Answering (Table QA) refers to providing precise answers from tables to answer a user's question. In recent years, there have been a lot of works on table QA, but there is a lack of comprehensive surveys on this research topic. Hence, we aim to provide an overview of available datasets and representative methods in table QA. We classify existing methods for table QA into five categories according to their techniques, which include semantic-parsing-based, generative, extractive, matching-based, and retriever-reader-based methods. Moreover, as table QA is still a challenging task for existing methods, we also identify and outline several key challenges and discuss the potential future directions of table QA.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering

    cs.CL 2025-09 conditional novelty 5.0 of 10

    TableZoomer compresses tables into schemas, zooms to query-relevant regions, and executes generated Python to answer questions, lifting Qwen3-8B accuracy by 19.34 points on DataBench and 25 points on TableBench Fact Checking.

  2. Agentic LLMs for Question Answering over Tabular Data

    cs.CL 2025-09 conditional novelty 4.0 of 10

    A five-stage NL-to-SQL pipeline with GPT-4o achieves 70.5% on DataBench QA and 71.6% on DataBench Lite QA, beating baselines of 26% and 27%.

  3. On the Comprehensibility of Multi-structured Financial Documents using LLMs and Pre-processing Tools

    cs.IR 2025-06 conditional novelty 4.0 of 10

    Preprocessing financial PDFs into text, tables, and chart data with existing tools improves LLM question-answering accuracy over direct GPT-4o image input in a small private evaluation.

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