LM Arena (Chatbot Arena)

What is LM Arena (Chatbot Arena)?

LM Arena, also known as Chatbot Arena, is an open-source platform developed by LMSYS and UC Berkeley SkyLab to advance the development and understanding of large language models through live, transparent, and community-driven evaluations. It enables users to interact with and compare multiple LLMs side-by-side in anonymous battles, collecting votes to rank models using the Elo rating system. The platform supports a wide range of publicly released models, including both open-weight and commercial APIs, and continuously updates its leaderboard based on real-world user feedback. LM Arena emphasizes transparency, open science, and collaboration by sharing datasets, evaluation tools, and infrastructure openly on GitHub.

Key Features

• Crowdsourced Pairwise Model Comparison

  Users engage in anonymous, randomized battles between two LLMs, voting on the better response to generate reliable comparative data.

• Elo Rating System for Model Ranking

  Adopts the widely recognized Elo rating system to provide dynamic, statistically sound rankings of LLM performance.

• Open-Source Infrastructure

  All platform components including frontend, backend, evaluation pipelines, and ranking algorithms are open source and publicly available.

• Live and Continuous Evaluation

  Real-time collection of user prompts and votes ensures up-to-date benchmarking reflecting current model capabilities and real-world use cases.

• Support for Publicly Released Models

  Includes models that are open-weight, publicly accessible via APIs, or available as services, ensuring transparency and reproducibility.

• Community Engagement and Transparency

  Encourages broad participation and openly shares user preference data and prompts to foster collaborative AI research.

Use Cases

• LLM Performance Benchmarking : Researchers and developers can evaluate and compare the effectiveness of various large language models under real-world conditions.
• Model Selection for Deployment : Organizations can identify the best-performing LLMs for their specific applications by reviewing live community-driven rankings.
• Open Science and Research : Academics and AI practitioners can access shared datasets and tools to conduct reproducible research and improve model development.
• Community Feedback for Model Improvement : Model providers can gather anonymized user feedback and voting data to refine and enhance their AI systems before official releases.

FAQs