Harnessing Multiple Large Language Models: A Survey on LLM Ensemble
Zhijun Chen, Xiaodong Lu, Jingzheng Li, Pengpeng Chen, Zhuoran Li, Kai Sun, Yuankai Luo, Qianren Mao
Abstract
LLM Ensemble---which involves the comprehensive use of multiple large language models (LLMs), each aimed at handling user queries during downstream inference, to benefit from their individual strengths---has gained substantial attention recently. The widespread availability of LLMs, coupled with their varying strengths and out-of-the-box usability, has profoundly advanced the field of LLM Ensemble. This paper presents the first systematic review of recent developments in LLM Ensemble. First, we introduce our taxonomy of LLM Ensemble and discuss several related research problems. Then, we provide a more in-depth classification of the methods under the broad categories of ``ensemble-before-inference, ensemble-during-inference, ensemble-after-inference'', and review relevant methods. Finally, we introduce related benchmarks and applications, summarize existing studies, and suggest several future research directions.
BibTeX
@inproceedings{ijcai2026_harnessingmultip,
title = {Harnessing Multiple Large Language Models: A Survey on LLM Ensemble},
author = {Zhijun Chen and Xiaodong Lu and Jingzheng Li and Pengpeng Chen and Zhuoran Li and Kai Sun and Yuankai Luo and Qianren Mao and Ming Li and Likang Xiao and Dingqi Yang and Yikun Ban and Hailong Sun},
booktitle = {IJCAI 2026},
year = {2026}
}