ReviewEval: An Evaluation Framework for AI-Generated Reviews

• URL: http://arxiv.org/abs/2502.11736v3
• Date: Sat, 24 May 2025 17:01:28 GMT
• Title: ReviewEval: An Evaluation Framework for AI-Generated Reviews
• Authors: Madhav Krishan Garg, Tejash Prasad, Tanmay Singhal, Chhavi Kirtani, Murari Mandal, Dhruv Kumar,
• Abstract summary: The escalating volume of academic research, coupled with a shortage of qualified reviewers, necessitates innovative approaches to peer review.<n>We propose ReviewEval, a comprehensive evaluation framework for AI-generated reviews that measures alignment with human assessments, verifies factual accuracy, assesses analytical depth, identifies degree of constructiveness and adherence to reviewer guidelines.<n>This paper establishes essential metrics for AIbased peer review and substantially enhances the reliability and impact of AI-generated reviews in academic research.
• 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. In this work, we propose: 1. ReviewEval, a comprehensive evaluation framework for AI-generated reviews that measures alignment with human assessments, verifies factual accuracy, assesses analytical depth, identifies degree of constructiveness and adherence to reviewer guidelines; and 2. ReviewAgent, an LLM-based review generation agent featuring a novel alignment mechanism to tailor feedback to target conferences and journals, along with a self-refinement loop that iteratively optimizes its intermediate outputs and an external improvement loop using ReviewEval to improve upon the final reviews. ReviewAgent improves actionable insights by 6.78% and 47.62% over existing AI baselines and expert reviews respectively. Further, it boosts analytical depth by 3.97% and 12.73%, enhances adherence to guidelines by 10.11% and 47.26% respectively. This paper establishes essential metrics for AIbased peer review and substantially enhances the reliability and impact of AI-generated reviews in academic research.

Related papers

• The AI Imperative: Scaling High-Quality Peer Review in Machine Learning [49.87236114682497]
  We argue that AI-assisted peer review must become an urgent research and infrastructure priority.<n>We propose specific roles for AI in enhancing factual verification, guiding reviewer performance, assisting authors in quality improvement, and supporting ACs in decision-making.
  arXiv  Detail & Related papers  (2025-06-09T18:37:14Z)
• Identifying Reliable Evaluation Metrics for Scientific Text Revision [7.503795054002405]
  Traditional metrics such as ROUGE and BERTScore primarily focus on similarity rather than capturing meaningful improvements.<n>We first conduct a manual annotation study to assess the quality of different revisions.<n>Then, we investigate reference-free evaluation metrics from related NLP domains.<n>We find that a hybrid approach combining LLM-as-a-judge evaluation and task-specific metrics offers the most reliable assessment of revision quality.
  arXiv  Detail & Related papers  (2025-06-05T09:00:23Z)
• Identifying Aspects in Peer Reviews [61.374437855024844]
  We develop a data-driven schema for deriving fine-grained aspects from a corpus of peer reviews. We introduce a dataset of peer reviews augmented with aspects and show how it can be used for community-level review analysis.
  arXiv  Detail & Related papers  (2025-04-09T14:14:42Z)
• Understanding and Supporting Peer Review Using AI-reframed Positive Summary [18.686807993563168]
  This study explored the impact of appending an automatically generated positive summary to the peer reviews of a writing task.<n>We found that adding an AI-reframed positive summary to otherwise harsh feedback increased authors' critique acceptance.<n>We discuss the implications of using AI in peer feedback, focusing on how it can influence critique acceptance and support research communities.
  arXiv  Detail & Related papers  (2025-03-13T11:22:12Z)
• ReviewAgents: Bridging the Gap Between Human and AI-Generated Paper Reviews [26.031039064337907]
  Academic paper review is a critical yet time-consuming task within the research community. With the increasing volume of academic publications, automating the review process has become a significant challenge. We propose ReviewAgents, a framework that leverages large language models (LLMs) to generate academic paper reviews.
  arXiv  Detail & Related papers  (2025-03-11T14:56:58Z)
• Is Your Paper Being Reviewed by an LLM? A New Benchmark Dataset and Approach for Detecting AI Text in Peer Review [6.20631177269082]
  We introduce a comprehensive dataset containing a total of 788,984 AI-written peer reviews paired with corresponding human reviews. We use this new resource to evaluate the ability of 18 existing AI text detection algorithms to distinguish between peer reviews written by humans and different state-of-the-art LLMs.
  arXiv  Detail & Related papers  (2025-02-26T23:04:05Z)
• Learning to Align Multi-Faceted Evaluation: A Unified and Robust Framework [61.38174427966444]
  Large Language Models (LLMs) are being used more and more extensively for automated evaluation in various scenarios. Previous studies have attempted to fine-tune open-source LLMs to replicate the evaluation explanations and judgments of powerful proprietary models. We propose a novel evaluation framework, ARJudge, that adaptively formulates evaluation criteria and synthesizes both text-based and code-driven analyses.
  arXiv  Detail & Related papers  (2025-02-26T06:31:45Z)
• DeepCRCEval: Revisiting the Evaluation of Code Review Comment Generation [11.010557279355885]
  This study empirically analyzes benchmark comments using a novel set of criteria informed by prior research and developer interviews.<n>Our evaluation framework, DeepCRCEval, integrates human evaluators and Large Language Models (LLMs) for a comprehensive reassessment of current techniques.
  arXiv  Detail & Related papers  (2024-12-24T08:53:54Z)
• Optimizing the role of human evaluation in LLM-based spoken document summarization systems [0.0]
  We propose an evaluation paradigm for spoken document summarization explicitly tailored for generative AI content. We provide detailed evaluation criteria and best practices guidelines to ensure robustness in the experimental design, replicability, and trustworthiness of human evaluations.
  arXiv  Detail & Related papers  (2024-10-23T18:37:14Z)
• An Automatic and Cost-Efficient Peer-Review Framework for Language Generation Evaluation [29.81362106367831]
  Existing evaluation methods often suffer from high costs, limited test formats, the need of human references, and systematic evaluation biases. In contrast to previous studies that rely on human annotations, Auto-PRE selects evaluators automatically based on their inherent traits. Experimental results indicate our Auto-PRE achieves state-of-the-art performance at a lower cost.
  arXiv  Detail & Related papers  (2024-10-16T06:06:06Z)
• STRICTA: Structured Reasoning in Critical Text Assessment for Peer Review and Beyond [68.47402386668846]
  We introduce Structured Reasoning In Critical Text Assessment (STRICTA) to model text assessment as an explicit, step-wise reasoning process.<n>STRICTA breaks down the assessment into a graph of interconnected reasoning steps drawing on causality theory.<n>We apply STRICTA to a dataset of over 4000 reasoning steps from roughly 40 biomedical experts on more than 20 papers.
  arXiv  Detail & Related papers  (2024-09-09T06:55:37Z)
• HD-Eval: Aligning Large Language Model Evaluators Through Hierarchical Criteria Decomposition [92.17397504834825]
  HD-Eval is a framework that iteratively aligns large language models evaluators with human preference. HD-Eval inherits the essence from the evaluation mindset of human experts and enhances the alignment of LLM-based evaluators. Extensive experiments on three evaluation domains demonstrate the superiority of HD-Eval in further aligning state-of-the-art evaluators.
  arXiv  Detail & Related papers  (2024-02-24T08:01:32Z)
• A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [55.33653554387953]
  Pattern Analysis and Machine Intelligence (PAMI) has led to numerous literature reviews aimed at collecting and fragmented information.<n>This paper presents a thorough analysis of these literature reviews within the PAMI field.<n>We try to address three core research questions: (1) What are the prevalent structural and statistical characteristics of PAMI literature reviews; (2) What strategies can researchers employ to efficiently navigate the growing corpus of reviews; and (3) What are the advantages and limitations of AI-generated reviews compared to human-authored ones.
  arXiv  Detail & Related papers  (2024-02-20T11:28:50Z)
• Learning and Evaluating Human Preferences for Conversational Head Generation [101.89332968344102]
  We propose a novel learning-based evaluation metric named Preference Score (PS) for fitting human preference according to the quantitative evaluations across different dimensions. PS can serve as a quantitative evaluation without the need for human annotation.
  arXiv  Detail & Related papers  (2023-07-20T07:04:16Z)
• From Static Benchmarks to Adaptive Testing: Psychometrics in AI Evaluation [60.14902811624433]
  We discuss a paradigm shift from static evaluation methods to adaptive testing. This involves estimating the characteristics and value of each test item in the benchmark and dynamically adjusting items in real-time. We analyze the current approaches, advantages, and underlying reasons for adopting psychometrics in AI evaluation.
  arXiv  Detail & Related papers  (2023-06-18T09:54:33Z)
• Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation [136.16507050034755]
  Existing human evaluation studies for summarization either exhibit a low inter-annotator agreement or have insufficient scale. We propose a modified summarization salience protocol, Atomic Content Units (ACUs), which is based on fine-grained semantic units. We curate the Robust Summarization Evaluation (RoSE) benchmark, a large human evaluation dataset consisting of 22,000 summary-level annotations over 28 top-performing systems.
  arXiv  Detail & Related papers  (2022-12-15T17:26:05Z)
• Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach [84.02388020258141]
  We propose a new framework named ENIGMA for estimating human evaluation scores based on off-policy evaluation in reinforcement learning. ENIGMA only requires a handful of pre-collected experience data, and therefore does not involve human interaction with the target policy during the evaluation. Our experiments show that ENIGMA significantly outperforms existing methods in terms of correlation with human evaluation scores.
  arXiv  Detail & Related papers  (2021-02-20T03:29:20Z)

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.