PeerCheck: Enhancing LLM-Generated Academic Reviews Towards Human-Level Quality
As academic submissions grow, the traditional peer review process struggles to keep up, raising concerns about quality and fairness. A trend of using large language models (LLMs) for assistance has emerged. In this work, we take a critical step toward improving the quality of LLM-generated reviews. We propose the PeerCheck framework, which investigates LLM-human review differences (RQ1) and explores methods to improve LLM-generated review quality (RQ2). We first analyzed the human-written reviews with reviews generated by various LLMs and found that LLMs and humans focus on different terms, e.g., LLMs prioritize theory while humans emphasize methodology and experiments. We further adopt p...
| Authors | Zeyuan Chen, Ziqing Yang, Yihan Ma et al. |
|---|---|
| Primary category | cs.CL |
| Published | 18 June 2026 |
| Last updated at source | 18 June 2026 |
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