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.
A Survey on Table Question Answering: Recent Advances
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
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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cs.CL 1years
2025 1verdicts
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unclear 1representative citing papers
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TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering
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.