LLM Review: Enhancing Creative Writing via Blind Peer Review Feedback
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915725650690048 |
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| author | Li, Weiyue Song, Mingxiao Shen, Zhenda Zhao, Dachuan Long, Yunfan Li, Yi Li, Yongce Yang, Ruyi Wang, Mengyu |
| author_facet | Li, Weiyue Song, Mingxiao Shen, Zhenda Zhao, Dachuan Long, Yunfan Li, Yi Li, Yongce Yang, Ruyi Wang, Mengyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_08003 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LLM Review: Enhancing Creative Writing via Blind Peer Review Feedback Li, Weiyue Song, Mingxiao Shen, Zhenda Zhao, Dachuan Long, Yunfan Li, Yi Li, Yongce Yang, Ruyi Wang, Mengyu Computation and Language Artificial Intelligence Multiagent Systems 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. |
| title | LLM Review: Enhancing Creative Writing via Blind Peer Review Feedback |
| topic | Computation and Language Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2601.08003 |