ReGround: Grounding Reviewer Comments in Multimodal Evidence

• URL: http://arxiv.org/abs/2609.11460v1
• Date: Thu, 10 Sep 2026 12:32:58 GMT
• Title: ReGround: Grounding Reviewer Comments in Multimodal Evidence
• Abstract summary: 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.<n>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.<n>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.
• Score: 65.14860452709777
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• ReviewGrounder: Improving Review Substantiveness with Rubric-Guided, Tool-Integrated Agents [50.27474750319121]
  Review text according to paper-specific rubrics derived from official guidelines, the paper's content, and human-written reviews.<n>We introduce REVIEWBENCH, a benchmark evaluating review text according to paper-specific rubrics derived from official guidelines, the paper's content, and human-written reviews.<n>We propose REVIEWGROUNDER, a rubric-guided, tool-integrated multi-agent framework that decomposes reviewing into drafting and grounding stages.
  arXiv  Detail & Related papers  (2026-04-15T16:33:04Z)
• FactReview: Evidence-Grounded Reviews with Literature Positioning and Execution-Based Claim Verification [57.196748998757954]
  We present FactReview, an evidence-grounded reviewing system that combines claim extraction, literature positioning, and execution-based claim verification.<n>Given a submission, FactReview identifies major claims and reported results, retrieves nearby work to clarify the paper's technical position, and, when code is available, executes the released repository.<n>It then produces a concise review and an evidence report that assigns each major claim one of five labels: Supported, Supported by the paper, Partially supported, In conflict, or Inconclusive.
  arXiv  Detail & Related papers  (2026-04-05T11:45:22Z)
• CiteAudit: You Cited It, But Did You Read It? A Benchmark for Verifying Scientific References in the LLM Era [51.63024682584688]
  Large language models (LLMs) introduce a new risk: fabricated references that appear plausible but correspond to no real publications.<n>We present the first comprehensive benchmark and detection framework for hallucinated citations in scientific writing.<n>Our framework significantly outperforms prior methods in both accuracy and interpretability.
  arXiv  Detail & Related papers  (2026-02-26T19:17:39Z)
• Scientific Opinion Summarization: Paper Meta-review Generation Dataset, Methods, and Evaluation [55.00687185394986]
  We propose the task of scientific opinion summarization, where research paper reviews are synthesized into meta-reviews. We introduce the ORSUM dataset covering 15,062 paper meta-reviews and 57,536 paper reviews from 47 conferences. Our experiments show that (1) human-written summaries do not always satisfy all necessary criteria such as depth of discussion, and identifying consensus and controversy for the specific domain, and (2) the combination of task decomposition and iterative self-refinement shows strong potential for enhancing the opinions.
  arXiv  Detail & Related papers  (2023-05-24T02:33:35Z)
• ReAct: A Review Comment Dataset for Actionability (and more) [0.8885727065823155]
  We introduce an annotated review comment dataset ReAct. The review comments are sourced from OpenReview site. We crowd-source annotations for these reviews for actionability and type of comments.
  arXiv  Detail & Related papers  (2022-10-02T07:09:38Z)
• AmbiFC: Fact-Checking Ambiguous Claims with Evidence [57.7091560922174]
  We present AmbiFC, a fact-checking dataset with 10k claims derived from real-world information needs. We analyze disagreements arising from ambiguity when comparing claims against evidence in AmbiFC. We develop models for predicting veracity handling this ambiguity via soft labels.
  arXiv  Detail & Related papers  (2021-04-01T17:40:08Z)
• Weakly- and Semi-supervised Evidence Extraction [107.47661281843232]
  We propose new methods to combine few evidence annotations with abundant document-level labels for the task of evidence extraction. Our approach yields substantial gains with as few as hundred evidence annotations.
  arXiv  Detail & Related papers  (2020-11-03T04:05:00Z)
• Massive Multi-Document Summarization of Product Reviews with Weak Supervision [11.462916848094403]
  Product reviews summarization is a type of Multi-Document Summarization (MDS) task. We show that summarizing small samples of the reviews can result in loss of important information. We propose a schema for summarizing a massive set of reviews on top of a standard summarization algorithm.
  arXiv  Detail & Related papers  (2020-07-22T11:22:57Z)
• How Useful are Reviews for Recommendation? A Critical Review and Potential Improvements [8.471274313213092]
  We investigate a growing body of work that seeks to improve recommender systems through the use of review text. Our initial findings reveal several discrepancies in reported results, partly due to copying results across papers despite changes in experimental settings or data pre-processing. Further investigation calls for discussion on a much larger problem about the "importance" of user reviews for recommendation.
  arXiv  Detail & Related papers  (2020-05-25T16:30:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.