Automatically Evaluating the Paper Reviewing Capability of Large Language Models

• URL: http://arxiv.org/abs/2502.17086v2
• Date: Thu, 24 Apr 2025 06:43:15 GMT
• Title: Automatically Evaluating the Paper Reviewing Capability of Large Language Models
• 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) show potential for providing assistance, but research has reported significant limitations in the reviews they generate.<n>We developed an automatic evaluation pipeline to assess the LLMs' paper review capability by comparing them with expert-generated reviews.
• Score: 46.0003776499898
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Peer review is essential for scientific progress, but it faces challenges such as reviewer shortages and growing workloads. Although Large Language Models (LLMs) show potential for providing assistance, research has reported significant limitations in the reviews they generate. While the insights are valuable, conducting the analysis is challenging due to the considerable time and effort required, especially given the rapid pace of LLM developments. To address the challenge, we developed an automatic evaluation pipeline to assess the LLMs' paper review capability by comparing them with expert-generated reviews. By constructing a dataset consisting of 676 OpenReview papers, we examined the agreement between LLMs and experts in their strength and weakness identifications. The results showed that LLMs lack balanced perspectives, significantly overlook novelty assessment when criticizing, and produce poor acceptance decisions. Our automated pipeline enables a scalable evaluation of LLMs' paper review capability over time.

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