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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.</abstract>
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%0 Conference Proceedings
%T ReGround: Grounding Reviewer Comments in Multimodal Evidence
%A Basch, Serwar
%A Qu, Lizhen
%A Gurevych, Iryna
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F basch-etal-2026-reground
%X 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.
%U https://aclanthology.org/2026.emnlp-main.54/
%P 1216-1234

Markdown (Informal)

[ReGround: Grounding Reviewer Comments in Multimodal Evidence](https://aclanthology.org/2026.emnlp-main.54/) (Basch et al., EMNLP 2026)

• ReGround: Grounding Reviewer Comments in Multimodal Evidence (Basch et al., EMNLP 2026)

ACL

• Serwar Basch, Lizhen Qu, and Iryna Gurevych. 2026. ReGround: Grounding Reviewer Comments in Multimodal Evidence. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 1216–1234, Budapest, Hungary. Association for Computational Linguistics.