Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

4.2 High agreement between GPT-4 and humans

1. How well did GPT-4 agree with human judges?
   On MT-Bench (58 expert raters, about 3K votes), GPT-4 agreed with humans 85% of the time on non-tie votes, more than humans agreed with each other (81%). On Chatbot Arena crowd votes the picture was similar. Agreement rose from about 70% toward 100% as the quality gap between the two models grew.
   • Zheng et al., 2023, Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena (Advances in Neural Information Processing Systems 36 (NeurIPS 2023), Datasets and Benchmarks Track), §4.2
2. What happened when human raters disagreed with GPT-4 and were shown its judgment?
   They found GPT-4’s reasoning reasonable in 75% of cases and changed their own choice in 34%.
   • Zheng et al., 2023, Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena (Advances in Neural Information Processing Systems 36 (NeurIPS 2023), Datasets and Benchmarks Track), §4.2
3. Why is GPT-4 single-answer grading attractive despite small losses?
   Its scores matched both pairwise GPT-4 and human preferences well, suggesting a fairly stable internal rubric, and it scales linearly with the number of models, unlike pairwise comparison.
   • Zheng et al., 2023, Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena (Advances in Neural Information Processing Systems 36 (NeurIPS 2023), Datasets and Benchmarks Track), §4.2