Judging LLM-as-a-judge with MT-Bench and Chatbot Arena
Authors: name: Lianmin Zheng, name: Wei-Lin Chiang, name: Ying Sheng, name: Siyuan Zhuang, name: Zhanghao Wu, name: Yonghao Zhuang, name: Zi Lin, name: Zhuohan Li, name: Dacheng Li, name: Eric. P Xing, name: Hao Zhang, name: Joseph E. Gonzalez, name: Ion Stoica

Evaluating large language model (LLM) based chat assistants is challenging
due to their broad capabilities and the inadequacy of existing benchmarks in
measuring human preferences. To address this, we explore using strong LLMs as
judges to evaluate these models on more open-ended questions. We examine the
usage and limitations of LLM-as-a-judge, such as position and verbosity biases
and limited reasoning ability, and propose solutions to migrate some of them.
We then verify the agreement between LLM judges and human preferences by
introducing two benchmarks: MT-bench, a multi-turn question set; and Chatbot
Arena, a crowdsourced battle platform. Our results reveal that strong LLM
judges like GPT-4 can match both controlled and crowdsourced human preferences
well, achieving over 80\% agreement, the same level of agreement between
humans. Hence, LLM-as-a-judge is a scalable and explainable way to approximate
human preferences, which are otherwise very expensive to obtain. Additionally,
we show our benchmark and traditional benchmarks complement each other by
evaluating several variants of LLaMA/Vicuna. We will publicly release 80
MT-bench questions, 3K expert votes, and 30K conversations with human
preferences from Chatbot Arena.