# Modern Question Answering Datasets and Benchmarks: A Survey

Source: [Modern Question Answering Datasets and Benchmarks: A Survey](<https://www.sota2.com/research/paper/68fa6cde1e209131cea36631>)

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

Data updated: 2025-10-28T13:56:54.085Z

Authors: Zhen Wang

Published: 2022-06-30T05:53:56.000Z

## Abstract

Question Answering (QA) is one of the most important natural language processing (NLP) tasks. It aims using NLP technologies to generate a corresponding answer to a given question based on the massive unstructured corpus. With the development of deep learning, more and more challenging QA datasets are being proposed, and lots of new methods for solving them are also emerging. In this paper, we investigate influential QA datasets that have been released in the era of deep learning. Specifically, we begin with introducing two of the most common QA tasks - textual question answer and visual question answering - separately, covering the most representative datasets, and then give some current challenges of QA research.

## 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/2206.15030>)

[PDF](<https://arxiv.org/pdf/2206.15030.pdf>)