Scoring, Reasoning, and Selecting the Best! Ensembling Large Language Models via a Peer-Review Process

• URL: http://arxiv.org/abs/2512.23213v1
• Date: Mon, 29 Dec 2025 05:25:49 GMT
• Title: Scoring, Reasoning, and Selecting the Best! Ensembling Large Language Models via a Peer-Review Process
• Authors: Zhijun Chen, Zeyu Ji, Qianren Mao, Junhang Cheng, Bangjie Qin, Hao Wu, Zhuoran Li, Jingzheng Li, Kai Sun, Zizhe Wang, Yikun Ban, Zhu Sun, Xiangyang Ji, Hailong Sun,
• Abstract summary: LLM-PeerReview is built on a novel, peer-review-inspired framework.<n>It operates in three stages: For scoring, we use the emerging LLM-as-a-Judge technique.<n>For reasoning, we can apply a graphical model-based truth inference algorithm.<n>Finally, the highest-scoring response is selected as the best ensemble output.
• Score: 58.265053900416895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose LLM-PeerReview, an unsupervised LLM Ensemble method that selects the most ideal response from multiple LLM-generated candidates for each query, harnessing the collective wisdom of multiple models with diverse strengths. LLM-PeerReview is built on a novel, peer-review-inspired framework that offers a clear and interpretable mechanism, while remaining fully unsupervised for flexible adaptability and generalization. Specifically, it operates in three stages: For scoring, we use the emerging LLM-as-a-Judge technique to evaluate each response by reusing multiple LLMs at hand; For reasoning, we can apply a principled graphical model-based truth inference algorithm or a straightforward averaging strategy to aggregate multiple scores to produce a final score for each response; Finally, the highest-scoring response is selected as the best ensemble output. LLM-PeerReview is conceptually simple and empirically powerful. The two variants of the proposed approach obtain strong results across four datasets, including outperforming the recent advanced model Smoothie-Global by 6.9% and 7.3% points, respectively.

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