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Open-WikiTable: Dataset for Open Domain Question Answering with Complex Reasoning over Table

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arxiv 2305.07288 v1 pith:BGOZTPPD submitted 2023-05-12 cs.CL

Sunjun Kweon , Yeonsu Kwon , Seonhee Cho , Yohan Jo , Edward Choi This is my paper
classification cs.CL
keywords open-wikitabledatasetquestionansweringanswerscomplexdatasetsdomain
verification ladder T0 review T1 audit T2 compute T3 formal
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Despite recent interest in open domain question answering (ODQA) over tables, many studies still rely on datasets that are not truly optimal for the task with respect to utilizing structural nature of table. These datasets assume answers reside as a single cell value and do not necessitate exploring over multiple cells such as aggregation, comparison, and sorting. Thus, we release Open-WikiTable, the first ODQA dataset that requires complex reasoning over tables. Open-WikiTable is built upon WikiSQL and WikiTableQuestions to be applicable in the open-domain setting. As each question is coupled with both textual answers and SQL queries, Open-WikiTable opens up a wide range of possibilities for future research, as both reader and parser methods can be applied. The dataset and code are publicly available.

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Cited by 6 Pith papers

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

  1. TableMoE: Neuro-Symbolic Routing for Structured Expert Reasoning in Multimodal Table Understanding

    cs.AI 2025-06 conditional novelty 6.0 of 10

    TableMoE is a multimodal table model whose role-aware router sends table tokens to HTML, JSON, and code experts and reports state-of-the-art results on its own WildStruct benchmarks and MMMU-Table.

  2. RelationalFactQA: A Benchmark for Evaluating Tabular Fact Retrieval from Large Language Models

    cs.CL 2025-05 conditional novelty 6.0 of 10

    RelationalFactQA shows that LLMs are much worse at retrieving facts as multi-record tables than as single answers, with the best model reaching only 24.7% tuple similarity.

  3. What to Keep and What to Drop: Adaptive Table Filtering Framework

    cs.CL 2025-06 conditional novelty 5.0 of 10

    ATF prunes table columns and rows with LLM scoring plus retrieval, cutting cells by about 70% and improving out-of-domain TableQA accuracy, while hurting in-domain QA and fact verification.

  4. DeALOG: Decentralized Multi-Agents Log-Mediated Reasoning Framework

    cs.CL 2026-02 reject novelty 4.0 of 10

    DeALOG lets five specialized LLM agents cooperate through a shared text log, and the paper claims this gives competitive zero-shot accuracy on six table/text/image QA benchmarks.

  5. OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering

    cs.CL 2025-07 conditional novelty 4.0 of 10

    A 4B-parameter model fine-tuned with supervised cold-start and an asynchronous GRPO reinforcement learning variant reaches 86.2% exact match on a held-out subset of Open WikiTable by using search and SQL tools.

  6. Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges

    cs.CL 2025-07 conditional novelty 3.0 of 10

    A structured review of table understanding with LLMs that proposes a taxonomy of input representations and identifies three research gaps.

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