MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures
<|reference_start|>MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures: Evaluating large language models (LLMs) is challenging. Traditional ground-truth-based benchmarks fail to capture the comprehensiveness and nuance of real-world queries, while LLM-as-judge benchmarks suffer from grading biases and limited query quantity. Both of them may also become contaminated over time. User-facing evaluation, such as Chatbot Arena, provides reliable signals but is costly and slow. In this work, we propose MixEval, a new paradigm for establishing efficient, gold-standard LLM evaluation by strategically mixing off-the-shelf benchmarks. It bridges (1) comprehensive and well-distributed real-world user queries and (2) efficient and fairly-graded ground-truth-based benchmarks, by matching queries mined from the web with similar queries from existing benchmarks. Based on MixEval, we further build MixEval-Hard, which offers more room for model improvement. Our benchmarks' advantages lie in (1) a 0.96 model ranking correlation with Chatbot Arena arising from the highly impartial query distribution and grading mechanism, (2) fast, cheap, and reproducible execution (6% of the time and cost of MMLU), and (3) dynamic evaluation enabled by the rapid and stable data update pipeline. We provide extensive meta-evaluation and analysis for our and existing LLM benchmarks to deepen the community's understanding of LLM evaluation and guide future research directions.<|reference_end|>
@article{ni2024mixeval:,
    title={MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures},
    author={Jinjie Ni, Fuzhao Xue, Xiang Yue, Yuntian Deng, Mahir Shah, Kabir
  Jain, Graham Neubig, Yang You},
    journal={arXiv preprint arXiv:2406.06565},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06565},
    primaryClass={cs.CL cs.AI cs.LG}
}