ReviewEval: An Evaluation Framework for AI-Generated Reviews

• URL: http://arxiv.org/abs/2502.11736v2
• Date: Fri, 21 Feb 2025 21:01:22 GMT
• Title: ReviewEval: An Evaluation Framework for AI-Generated Reviews
• Authors: Chhavi Kirtani, Madhav Krishan Garg, Tejash Prasad, Tanmay Singhal, Murari Mandal, Dhruv Kumar,
• Abstract summary: This research introduces a comprehensive evaluation framework for AI-generated reviews.<n>It measures alignment with human evaluations, verifies factual accuracy, assesses analytical depth, and identifies actionable insights.<n>Our framework establishes standardized metrics for evaluating AI-based review systems.
• Score: 9.35023998408983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The escalating volume of academic research, coupled with a shortage of qualified reviewers, necessitates innovative approaches to peer review. While large language model (LLMs) offer potential for automating this process, their current limitations include superficial critiques, hallucinations, and a lack of actionable insights. This research addresses these challenges by introducing a comprehensive evaluation framework for AI-generated reviews, that measures alignment with human evaluations, verifies factual accuracy, assesses analytical depth, and identifies actionable insights. We also propose a novel alignment mechanism that tailors LLM-generated reviews to the unique evaluation priorities of individual conferences and journals. To enhance the quality of these reviews, we introduce a self-refinement loop that iteratively optimizes the LLM's review prompts. Our framework establishes standardized metrics for evaluating AI-based review systems, thereby bolstering the reliability of AI-generated reviews in academic research.

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