Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference
Wei-Lin Chiang Lianmin Zheng Ying Sheng Anastasios Nikolas Angelopoulos Tianle Li Dacheng Li Hao Zhang Banghua Zhu Michael Jordan Joseph E. Gonzalez Ion Stoica
Large Language Models (LLMs) have unlocked new capabilities and applications;
however, evaluating the alignment with human preferences still poses
significant challenges. To address this issue, we introduce Chatbot Arena, an
open platform for evaluating LLMs based on human preferences. Our methodology
employs a pairwise comparison approach and leverages input from a diverse user
base through crowdsourcing. The platform has been operational for several
months, amassing over 240K votes. This paper describes the platform, analyzes
the data we have collected so far, and explains the tried-and-true statistical
methods we are using for efficient and accurate evaluation and ranking of
models. We confirm that the crowdsourced questions are sufficiently diverse and
discriminating and that the crowdsourced human votes are in good agreement with
those of expert raters. These analyses collectively establish a robust
foundation for the credibility of Chatbot Arena. Because of its unique value
and openness, Chatbot Arena has emerged as one of the most referenced LLM
leaderboards, widely cited by leading LLM developers and companies. Our demo is
publicly available at \url{https://chat.lmsys.org}.