EchoReview: Learning Peer Review from the Echoes of Scientific Citations

2026-01-31

AI Summary

This study addresses the growing strain on traditional peer review caused by the surge in scientific submissions and the limitations of existing automated approaches, which are hindered by the scarcity and subjectivity of human-generated review data. To overcome these challenges, we propose EchoReview, a novel framework that introduces a citation-context-based paradigm for synthesizing peer review data. By extracting implicit evaluative signals from academic citations, we construct EchoReview-16K—the first large-scale, cross-conference, and cross-year citation-driven review dataset—and fine-tune a 7B-parameter large language model to obtain EchoReviewer-7B. Experimental results demonstrate that our model consistently and significantly outperforms current methods on key dimensions such as evidential support and comprehensiveness, thereby validating citation context as a high-quality data source for automated peer review.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

As the volume of scientific submissions continues to grow rapidly, traditional peer review systems are facing unprecedented scalability pressures, highlighting the urgent need for automated reviewing methods that are both scalable and reliable. Existing supervised fine-tuning approaches based on real review data are fundamentally constrained by single-source of data as well as the inherent subjectivity and inconsistency of human reviews, limiting their ability to support high-quality automated reviewers. To address these issues, we propose EchoReview, a citation-context-driven data synthesis framework that systematically mines implicit collective evaluative signals from academic citations and transforms scientific community's long-term judgments into structured review-style data. Based on this pipeline, we construct EchoReview-16K, the first large-scale, cross-conference, and cross-year citation-driven review dataset, and train an automated reviewer, EchoReviewer-7B. Experimental results demonstrate that EchoReviewer-7B can achieve significant and stable improvements on core review dimensions such as evidence support and review comprehensiveness, validating citation context as a robust and effective data paradigm for reliable automated peer review.

Problem

• peer review
• automated reviewing
• citation context
• scalability
• review quality

Innovation

• citation-context-driven
• data synthesis
• automated peer review
• collective evaluative signals
• large language model