ReGround: Grounding Reviewer Comments in Multimodal Evidence
Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.
Computer Science > Computation and Language
arXiv:2609.11460 (cs)
[Submitted on 10 Sep 2026]
Title:ReGround: Grounding Reviewer Comments in Multimodal Evidence
View a PDF of the paper titled ReGround: Grounding Reviewer Comments in Multimodal Evidence, by Serwar Basch and 2 other authors
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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.
| Comments: | Accepted at EMNLP 2026 |
| Subjects: | Computation and Language (cs.CL); Information Retrieval (cs.IR) |
| Cite as: | arXiv:2609.11460 [cs.CL] |
| (or arXiv:2609.11460v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.11460
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled ReGround: Grounding Reviewer Comments in Multimodal Evidence, by Serwar Basch and 2 other authors
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