Summary of Wildbench: Benchmarking Llms with Challenging Tasks From Real Users in the Wild, by Bill Yuchen Lin et al.
WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild
by Bill Yuchen Lin, Yuntian Deng, Khyathi Chandu, Faeze Brahman, Abhilasha Ravichander, Valentina Pyatkin, Nouha Dziri, Ronan Le Bras, Yejin Choi
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper introduces WildBench, a framework designed to evaluate large language models (LLMs) using real-world user queries. The framework consists of 1,024 tasks selected from human-chatbot conversation logs and uses two metrics: WB-Reward and WB-Score. These metrics are computable using advanced LLMs like GPT-4-turbo and provide reliable and interpretable automatic judgments. The evaluation process employs task-specific checklists to systematically evaluate model outputs and provides structured explanations justifying the scores and comparisons. WildBench results demonstrate a strong correlation with human-voted Elo ratings from Chatbot Arena on hard tasks, with WB-Reward achieving a Pearson correlation of 0.98 with top-ranking models. Additionally, WB-Score reaches 0.95, surpassing other evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special tool to test how well large language models (LLMs) work. The tool is called WildBench and it uses real conversations between humans and chatbots to see if the models can do a good job of understanding what people want. The tool has two ways to measure how well the models are doing: WB-Reward and WB-Score. These measurements help us know if the models are really good at understanding people or not. The tool shows that some LLMs are much better than others at understanding what people want, and it even compares them to human chatbot conversations. The results are very useful for figuring out which LLMs are the best. |
Keywords
» Artificial intelligence » Gpt
