LLM-Based Scientific Peer Review: Methods, Benchmarks, and Reliability Challenges

2026-06-23

AI Summary

Traditional peer review faces scalability bottlenecks, while large language model (LLM)-driven automated review lacks systematic investigation into its reliability, robustness, and security. This work addresses this gap by offering the first system-oriented analysis, focusing on two core tasks: critique generation and score prediction. It establishes a taxonomy of LLM-based reviewing approaches and comprehensively evaluates key technical strategies, including prompt engineering, supervised fine-tuning, retrieval augmentation, and alignment optimization. The study uncovers emerging security threats such as prompt injection and data poisoning, examines challenges arising from subjective disagreement and cross-domain generalization, and highlights limitations and domain biases in current benchmarks. Building on these insights, the paper proposes a roadmap toward developing reliable, transparent, and trustworthy AI-assisted scientific review systems.

Abstract

The rapid growth of scientific submissions has pushed traditional peer review toward its scalability limits, motivating the exploration of large language models (LLMs) as intelligent automated evaluation assistants. Although recent studies show that LLMs can generate fluent critiques and approximate reviewer scores, their reliability, robustness, and security as decision-support systems remain insufficiently understood. This survey offers a systems-level analysis of LLM-based scientific peer review, focusing on two core evaluative functions: critique generation and score prediction. We present a structured taxonomy of modeling approaches (including prompt-based, supervised, retrieval-augmented, and alignment-optimized approaches), and synthesize empirical findings across existing benchmarks. We analyze dataset constraints, evaluation shortcomings, and domain concentration biases that limit current assessment practices. Beyond performance metrics, we identify emerging robustness risks, including prompt injection, data poisoning, retrieval vulnerabilities, and reward hacking, which expose automated review pipelines to strategic manipulation. From a data mining perspective, we outline key open challenges in modeling subjective disagreement and cross-domain generalization. By reframing automated peer review as a high-stakes, multi-objective decision problem, this survey provides a roadmap for developing robust, transparent, and trustworthy AI-assisted scientific evaluation systems.

Problem

• LLM-based peer review
• reliability
• robustness
• security
• scientific evaluation

Innovation

• LLM-based peer review
• robustness risks
• retrieval-augmented generation
• alignment-optimized models
• automated scientific evaluation