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arXiv · 2601.08003

LLM Review: Enhancing Creative Writing via Blind Peer Review Feedback

Weiyue Li·Mingxiao Song·Zhenda Shen·Dachuan Zhao·Yunfan Long·Yi Li·Yongce Li·Ruyi Yang·Mengyu Wang
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Abstract

Large Language Models (LLMs) often struggle with creative generation, and multi-agent frameworks that improve reasoning through interaction can paradoxically hinder creativity by inducing content homogenization. We introduce LLM Review, a peer-review-inspired framework implementing Blind Peer Review: agents exchange targeted feedback while revising independently, preserving divergent creative trajectories. To enable rigorous evaluation, we propose SciFi-100, a science fiction writing dataset with a unified framework combining LLM-as-a-judge scoring, human annotation, and rule-based novelty metrics. Experiments demonstrate that LLM Review consistently outperforms multi-agent baselines, and smaller models with our framework can surpass larger single-agent models, suggesting interaction structure may substitute for model scale.

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Weiyue Li, Mingxiao Song, Zhenda Shen, Dachuan Zhao, Yunfan Long, Yi Li, Yongce Li, Ruyi Yang, Mengyu Wang. 2026-01-12. LLM Review: Enhancing Creative Writing via Blind Peer Review Feedback. https://arxiv.org/abs/2601.08003

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