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Evidence record 785 · automatically gathered

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 review

Record details

Published: 18 June 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Finance, VC & PE
Retrieved: 14 July 2026

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How to cite this record

ethics.ai (18 June 2026), “PeerCheck: Enhancing LLM-Generated Academic Reviews Towards Human-Level Quality,” evidence record 785, https://ethics.ai/record/785 (originally published by arXiv).

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