Summary of Chatbot Arena: An Open Platform For Evaluating Llms by Human Preference, By Wei-lin Chiang et al.
Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference
by 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
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Chatbot Arena, an open platform for evaluating Large Language Models (LLMs) based on human preferences. The 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. The paper describes the platform, analyzes the data collected so far, and explains statistical methods used for evaluation and ranking of models. It confirms that crowdsourced questions are diverse and discriminating, and that human votes agree with those of expert raters. This establishes a robust foundation for the credibility of Chatbot Arena. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Chatbot Arena is a new way to evaluate how well large language models work like humans do. Right now, it’s hard to know if these models are actually helping people or just doing what they’re programmed to do. The platform lets users compare different models and vote on which one is the best. This helps us figure out what makes a good model. After being open for a few months, over 240,000 people have voted. The results show that the questions used are good at helping us understand how well the models work. This is important because it means we can trust the rankings on Chatbot Arena. |
