We present PeerQA, a real-world, scientific, document-level Question Answering (QA) dataset. PeerQA questions have been sourced from peer reviews, which contain questions that reviewers raised while thoroughly examining the scientific article. Answers have been annotated by the original authors of each paper. The dataset contains 579 QA pairs from 208 academic articles, with a majority from ML and NLP, as well as a subset of other scientific communities like Geoscience and Public Health. PeerQA supports three critical tasks for developing practical QA systems: Evidence retrieval, unanswerable question classification, and answer generation. We provide a detailed analysis of the collected dataset and conduct experiments establishing baseline systems for all three tasks. Our experiments and analyses reveal the need for decontextualization in document-level retrieval, where we find that even simple decontextualization approaches consistently improve retrieval performance across architectures. On answer generation, PeerQA serves as a challenging benchmark for long-context modeling, as the papers have an average size of 12k tokens. Our code and data is available at https://github.com/UKPLab/peerqa.

本研究提出了PeerQA，一个来自同行评审的真实世界科学文档级问答数据集，解决了科学文章评审过程中提出的问题及其回答的缺乏。该数据集包含579个问答对，支持证据检索、无法回答问题分类和答案生成等关键任务。实验结果显示，去上下文化的方法能有效提升文档检索性能，PeerQA为长上下文建模提供了具有挑战性的基准。

PeerQA：来自同行评审的科学问题回答数据集