# A Survey on Table Question Answering: Recent Advances

Source: [A Survey on Table Question Answering: Recent Advances](<https://www.sota2.com/research/paper/68fa6cff1e209131cea372ff>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:54.400Z

Authors: Nengzheng Jin, Joanna Siebert, Dongfang Li, Qingcai Chen

Published: 2022-07-12T02:44:40.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2207.05270>)

[PDF](<https://arxiv.org/pdf/2207.05270.pdf>)