Reviewing the Reviewer: Elevating Peer Review Quality through LLM-Guided Feedback

• URL: http://arxiv.org/abs/2602.10118v1
• Date: Sat, 17 Jan 2026 20:32:18 GMT
• Title: Reviewing the Reviewer: Elevating Peer Review Quality through LLM-Guided Feedback
• Authors: Sukannya Purkayastha, Qile Wan, Anne Lauscher, Lizhen Qu, Iryna Gurevych,
• Abstract summary: We introduce an LLM-driven framework that decomposes reviews into argumentative segments.<n>We also release LazyReviewPlus, a dataset of 1,309 sentences labeled for lazy thinking and specificity.
• Score: 75.31379834079648
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Peer review is central to scientific quality, yet reliance on simple heuristics -- lazy thinking -- has lowered standards. Prior work treats lazy thinking detection as a single-label task, but review segments may exhibit multiple issues, including broader clarity problems, or specificity issues. Turning detection into actionable improvements requires guideline-aware feedback, which is currently missing. We introduce an LLM-driven framework that decomposes reviews into argumentative segments, identifies issues via a neurosymbolic module combining LLM features with traditional classifiers, and generates targeted feedback using issue-specific templates refined by a genetic algorithm. Experiments show our method outperforms zero-shot LLM baselines and improves review quality by up to 92.4\%. We also release LazyReviewPlus, a dataset of 1,309 sentences labeled for lazy thinking and specificity.

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