PeerCheck: Enhancing LLM-Generated Academic Reviews Towards Human-Level Quality

• URL: http://arxiv.org/abs/2606.20897v1
• Date: Thu, 18 Jun 2026 19:45:28 GMT
• Title: PeerCheck: Enhancing LLM-Generated Academic Reviews Towards Human-Level Quality
• Abstract summary: We propose the PeerCheck framework, which investigates LLM-human review differences (RQ1) and explores methods to improve LLM-generated review quality (RQ2).<n>We first analyzed the human-written reviews with reviews generated by various LLMs and found that LLMs and humans focus on different terms.<n>We adopt prompt engineering, such as Chain-of-Thought (CoT), and utilize retrieval-augmented generation (RAG) to enhance the LLM-generated reviews towards human-level quality.
• Score: 26.831710344670913
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 prompt engineering, such as Chain-of-Thought (CoT), and utilize retrieval-augmented generation (RAG) to enhance the LLM-generated reviews towards human-level quality. We find CoT significantly improves the quality of LLM reviews, while we discover an unexpected "RAG paradox," i.e., experiments with RAG produce different results for various LLMs and, in some cases, even reduce review quality. Our comprehensive analysis of LLM-generated academic reviews illustrates both possibilities and limitations, contributing to a more effective, human-aligned review system. Our dataset is available on https://github.com/TrustAIRLab/PeerCheck.

Related papers

• Review Arcade: On the Human Alignment and Gameability of LLM Reviews [4.626261940793026]
  We perform experiments on papers from the 2025 ACL Rolling Review (ARR) to evaluate LLM reviews from both the author and the reviewer perspective.<n>We find that this "gaming" of LLM reviews can be effective in specific scenarios, leading to a statistically significant increase of overall scores for up to 35% of papers.
  arXiv  Detail & Related papers  (2026-05-27T12:40:35Z)
• Do LLMs Favor LLMs? Quantifying Interaction Effects in Peer Review [23.244156664404205]
  We provide the first comprehensive analysis of LLM use across the peer review pipeline.<n>We analyze over 125,000 paper-review pairs from ICLR, NeurIPS, and ICML.
  arXiv  Detail & Related papers  (2026-01-28T18:50:54Z)
• Reviewing the Reviewer: Elevating Peer Review Quality through LLM-Guided Feedback [75.31379834079648]
  We introduce an LLM-driven framework that decomposes reviews into argumentative segments.<n>We also release LazyReviewPlus, a dataset of 1,309 sentences labeled for lazy thinking and specificity.
  arXiv  Detail & Related papers  (2026-01-17T20:32:18Z)
• LLM-REVal: Can We Trust LLM Reviewers Yet? [70.58742663985652]
  Large language models (LLMs) have inspired researchers to integrate them extensively into the academic workflow.<n>This study focuses on how the deep integration of LLMs into both peer-review and research processes may influence scholarly fairness.
  arXiv  Detail & Related papers  (2025-10-14T10:30:20Z)
• Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review [66.73247554182376]
  Large language models (LLMs) have led to their integration into peer review.<n>The unchecked adoption of LLMs poses significant risks to the integrity of the peer review system.<n>We show that manipulating 5% of the reviews could potentially cause 12% of the papers to lose their position in the top 30% rankings.
  arXiv  Detail & Related papers  (2024-12-02T16:55:03Z)
• AI-Driven Review Systems: Evaluating LLMs in Scalable and Bias-Aware Academic Reviews [18.50142644126276]
  We evaluate the alignment of automatic paper reviews with human reviews using an arena of human preferences by pairwise comparisons. We fine-tune an LLM to predict human preferences, predicting which reviews humans will prefer in a head-to-head battle between LLMs. We make the reviews of publicly available arXiv and open-access Nature journal papers available online, along with a free service which helps authors review and revise their research papers and improve their quality.
  arXiv  Detail & Related papers  (2024-08-19T19:10:38Z)
• DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation [75.81096662788254]
  Large Language Models (LLMs) are scalable and economical evaluators.<n>The question of how reliable these evaluators are has emerged as a crucial research question.<n>We propose Decompose and Aggregate, which breaks down the evaluation process into different stages based on pedagogical practices.
  arXiv  Detail & Related papers  (2024-05-24T08:12:30Z)
• PRE: A Peer Review Based Large Language Model Evaluator [14.585292530642603]
  Existing paradigms rely on either human annotators or model-based evaluators to evaluate the performance of LLMs. We propose a novel framework that can automatically evaluate LLMs through a peer-review process.
  arXiv  Detail & Related papers  (2024-01-28T12:33:14Z)
• Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves [57.974103113675795]
  We present a method named Rephrase and Respond' (RaR) which allows Large Language Models to rephrase and expand questions posed by humans. RaR serves as a simple yet effective prompting method for improving performance. We show that RaR is complementary to the popular Chain-of-Thought (CoT) methods, both theoretically and empirically.
  arXiv  Detail & Related papers  (2023-11-07T18:43:34Z)
• Evaluating Large Language Models at Evaluating Instruction Following [54.49567482594617]
  We introduce a challenging meta-evaluation benchmark, LLMBar, designed to test the ability of an LLM evaluator in discerning instruction-following outputs. We discover that different evaluators exhibit distinct performance on LLMBar and even the highest-scoring ones have substantial room for improvement.
  arXiv  Detail & Related papers  (2023-10-11T16:38:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.