ReGround: Grounding Reviewer Comments in Multimodal Evidence

2026-09-10

AI Summary

本文针对评审意见难以与长多模态文档中的具体证据关联的问题，构建了ReGround数据集，并通过多种检索方法进行评估。

Abstract

Reviewer comments naturally relate to specific parts of the reviewed paper, yet grounding these comments to the underlying evidence is difficult due to long multimodal documents. Existing benchmarks do not capture this setting and largely focus on explicit, information-seeking queries. We introduce ReGround, a large-scale dataset for reviewer comment grounding that links 10,267 reviewer comments to 16,274 evidence in the original anonymous submission of 3,656 papers. We build on a simple observation: author rebuttals often include explicit references to content of the submission used to address reviewer comments, providing a high-precision annotation source. We cast grounding as a retrieval task and evaluate a wide range of retrieval methods. Results show that retrieval over the entire paper content performs poorly, evidence-type inference is a major bottleneck, and multimodal evidence provides complementary signals that text alone misses. Our dataset exposes grounding reviewer comments as a difficult and practically important problem for scientific document understanding.

Problem

• Reviewer Comments
• Multimodal Evidence
• Grounding

Innovation

• Reviewer Comment Grounding
• Multimodal Evidence
• Retrieval Task