Reviewing the Reviewer: Elevating Peer Review Quality through LLM-Guided Feedback
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914321038049280 |
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| author | Purkayastha, Sukannya Wan, Qile Lauscher, Anne Qu, Lizhen Gurevych, Iryna |
| author_facet | Purkayastha, Sukannya Wan, Qile Lauscher, Anne Qu, Lizhen Gurevych, Iryna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_10118 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reviewing the Reviewer: Elevating Peer Review Quality through LLM-Guided Feedback Purkayastha, Sukannya Wan, Qile Lauscher, Anne Qu, Lizhen Gurevych, Iryna Computation and Language Computers and Society 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. |
| title | Reviewing the Reviewer: Elevating Peer Review Quality through LLM-Guided Feedback |
| topic | Computation and Language Computers and Society |
| url | https://arxiv.org/abs/2602.10118 |