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LLM aspect prediction: reviewing academic papers from different aspects with Large Language Model
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DOI：10.1007/s11192-026-05771-2.png)
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Abstract
En 中文
Peer review is a fundamental process in scholarly publishing, wherein reviewers assess and score various aspects of a manuscript (e.g., novelty, clarity, and significance) based on established evaluation criteria. However, this process demands substantial time and effort, and remains inherently susceptible to human bias and inconsistency. To address this issue, we propose LLMAspectPrediction, a novel framework designed to predict fine-grained aspect scores for academic papers. This framework assists reviewers by providing consistent, criteria-driven assessments and offers authors actionable feedback aligned with peer review standards. The proposed methodology comprises three sequential stages. First, raw manuscript texts are preprocessed, and a vector database is utilized to retrieve topically similar papers from an external corpus to enrich the contextual input. Second, prompt templates anchored in standard peer review rubrics guide a Large Language Model (LLM) in generating evaluations for specific aspects. Finally, these LLM-generated evaluations serve as weak supervision signals to fine-tune a pre-trained model for robust score prediction. Experimental results demonstrate that our approach achieves state-of-the-art performance, and ablation studies confirm the critical contribution of each component to the overall model efficacy.
Keywords:
Large Language Model
Score prediction
Peer review
Pretrained language model
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