Beyond "Not Novel Enough": Enriching Scholarly Critique with LLM-Assisted Feedback

TL;DR

This paper introduces a structured, LLM-assisted method for automated novelty assessment in peer review, improving consistency and transparency in evaluating scholarly submissions, especially in high-volume fields like NLP.

Contribution

It presents a novel three-stage approach modeling expert reviewer behavior for automated novelty evaluation, outperforming existing LLM baselines and enhancing review consistency.

Findings

Achieves 86.5% alignment with human reasoning

Reaches 75.3% agreement on novelty conclusions

Outperforms existing LLM-based baselines

Abstract

Novelty assessment is a central yet understudied aspect of peer review, particularly in high volume fields like NLP where reviewer capacity is increasingly strained. We present a structured approach for automated novelty evaluation that models expert reviewer behavior through three stages: content extraction from submissions, retrieval and synthesis of related work, and structured comparison for evidence based assessment. Our method is informed by a large scale analysis of human written novelty reviews and captures key patterns such as independent claim verification and contextual reasoning. Evaluated on 182 ICLR 2025 submissions with human annotated reviewer novelty assessments, the approach achieves 86.5% alignment with human reasoning and 75.3% agreement on novelty conclusions - substantially outperforming existing LLM based baselines. The method produces detailed, literature aware analyses and improves consistency over ad hoc reviewer judgments. These results highlight the potential for structured LLM assisted approaches to support more rigorous and transparent peer review without displacing human expertise. Data and code are made available.