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arXiv Computation and Language By Sukannya Purkayastha, Qile Wan, Anne Lauscher, Lizhen Qu, Iryna Gurevych

Reviewing the Reviewer: LLM-Assisted Reviewer Feedback Generation for Guideline Compliance

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The paper presents an LLM-driven framework that splits peer reviews into argumentative segments, detects multiple co-occurring issues such as lazy thinking and lack of specificity, and generates targeted, guideline-aware feedback using issue-specific templates. An iterative, reranking-based generation algorithm refines the feedback, and a controlled rewriting study shows it can reduce guideline violations by up to 92.4%. The authors also release LazyReviewPlus, a multi-label dataset of 1,309 sentences annotated for detecting lazy thinking and lack of specificity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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