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 transparent 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 straightforward averaging strategy or a principled graphical model-based truth inference algorithm 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. Our results across four datasets show that the two variants of the proposed approach outperform the advanced model Smoothie-Global by 6.9% and 7.3% points, cross diverse task types including factual recall QA, math reasoning, and instruction following.
@article{arxiv.2512.23213,
title = {Scoring, Reasoning, and Selecting the Best! Ensembling Large Language Models via a Peer-Review Process},
author = {Zhijun Chen and Zeyu Ji and Qianren Mao and Hao Wu and Jinhuan Song and Junhang Cheng and Bangjie Qin and Zhuoran Li and Jingzheng Li and Kai Sun and Zizhe Wang and Yikun Ban and Zhu Sun and Xiangyang Ji and Hailong Sun},
journal= {arXiv preprint arXiv:2512.23213},
year = {2026}
}