LMArena Team

Created by researchers from UC Berkeley’s SkyLab, LMArena is an open platform where everyone can easily access, explore and interact with the world’s leading AI models.

RepoChat Arena

RepoChat Arena

RepoChat lets models automatically retrieve relevant files from the given GitHub repository. It can resolve issues, review PRs, implement code, as well as answer higher level questions about the repositories-all without requiring users to provide extensive context.

Arena Explorer

Arena Explorer

We developed a topic modeling pipeline and the Arena Explorer. This pipeline organizes user prompts into distinct topics, structuring the text data hierarchically to enable intuitive analysis. We believe this tool for hierarchical topic modeling can be valuable to anyone analyzing complex text data.

Code Editing in Copilot Arena

Code Editing in Copilot Arena

Copilot Arena enables not only paired code completions but also paired code edits as well. Unlike code completions—which automatically appear after short pauses—code edits are manually triggered by highlighting a code snippet and then writing a short task description.

Catch me if you can! How to beat GPT-4 with a 13B model

Catch me if you can! How to beat GPT-4 with a 13B model

Authors Shuo Yang* Wei-Lin Chiang* Lianmin Zheng* Joseph E. Gonzalez Ion Stoica Announcing Llama-rephraser: 13B models reaching GPT-4 performance in major benchmarks (MMLU/GSK-8K/HumanEval)! To ensure result validity, we followed OpenAI’s decontamination method and found no evidence of data contamination. What’s the trick behind it? Well, rephrasing

Copilot Arena

Copilot Arena

Copilot Arena has been downloaded 2.5K times on the VSCode Marketplace, served over 100K completions, and accumulated over 10K code completion battles.

Chatbot Arena Categories

Chatbot Arena Categories

By grouping tasks into categories, we can assess models’ strengths and weaknesses in a more granular way.

Preference Proxy Evaluations

Preference Proxy Evaluations

Most LLMs are optimized using an LLM judge or reward model to approximate human preference. These training processes can cost hundreds of thousands or millions of dollars. How can we know whether to trust an LLM judge or reward model, given its critical role in guiding LLM training?