Mind the Blind Spots: A Focus-Level Evaluation Framework for LLM Reviews

• URL: http://arxiv.org/abs/2502.17086v3
• Date: Fri, 23 May 2025 01:21:39 GMT
• Title: Mind the Blind Spots: A Focus-Level Evaluation Framework for LLM Reviews
• Authors: Hyungyu Shin, Jingyu Tang, Yoonjoo Lee, Nayoung Kim, Hyunseung Lim, Ji Yong Cho, Hwajung Hong, Moontae Lee, Juho Kim,
• Abstract summary: Large Language Models (LLMs) can automatically draft reviews now.<n> determining whether LLM-generated reviews are trustworthy requires systematic evaluation.<n>We introduce a focus-level evaluation framework that operationalizes the focus as a normalized distribution of attention.
• Score: 46.0003776499898
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Peer review underpins scientific progress, but it is increasingly strained by reviewer shortages and growing workloads. Large Language Models (LLMs) can automatically draft reviews now, but determining whether LLM-generated reviews are trustworthy requires systematic evaluation. Researchers have evaluated LLM reviews at either surface-level (e.g., BLEU and ROUGE) or content-level (e.g., specificity and factual accuracy). Yet it remains uncertain whether LLM-generated reviews attend to the same critical facets that human experts weigh -- the strengths and weaknesses that ultimately drive an accept-or-reject decision. We introduce a focus-level evaluation framework that operationalizes the focus as a normalized distribution of attention across predefined facets in paper reviews. Based on the framework, we developed an automatic focus-level evaluation pipeline based on two sets of facets: target (e.g., problem, method, and experiment) and aspect (e.g., validity, clarity, and novelty), leveraging 676 paper reviews (https://figshare.com/s/d5adf26c802527dd0f62) from OpenReview that consists of 3,657 strengths and weaknesses identified from human experts. The comparison of focus distributions between LLMs and human experts showed that the off-the-shelf LLMs consistently have a more biased focus towards examining technical validity while significantly overlooking novelty assessment when criticizing papers.

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