Sample-Efficient Human Evaluation of Large Language Models via Maximum Discrepancy Competition

• URL: http://arxiv.org/abs/2404.08008v2
• Date: Thu, 29 May 2025 13:16:05 GMT
• Title: Sample-Efficient Human Evaluation of Large Language Models via Maximum Discrepancy Competition
• Authors: Kehua Feng, Keyan Ding, Hongzhi Tan, Kede Ma, Zhihua Wang, Shuangquan Guo, Yuzhou Cheng, Ge Sun, Guozhou Zheng, Qiang Zhang, Huajun Chen,
• Abstract summary: We propose a sample-efficient human evaluation method for large language models (LLMs)<n>Our method automatically and adaptively selects a compact set of input instructions that maximize semantic discrepancy between pairs of LLM responses.<n>Human evaluators then perform three-alternative forced choices on these paired responses, which are aggregated into a global ranking using Elo rating.
• Score: 38.822535662755314
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reliable evaluation of large language models (LLMs) is impeded by two key challenges: objective metrics often fail to reflect human perception of natural language, and exhaustive human labeling is prohibitively expensive. Here, we propose a sample-efficient human evaluation method for LLMs based on the principle of MAximum Discrepancy (MAD) Competition. Our method automatically and adaptively selects a compact set of input instructions that maximize semantic discrepancy between pairs of LLM responses. Human evaluators then perform three-alternative forced choices on these paired responses, which are aggregated into a global ranking using Elo rating. We apply our approach to compare eight widely used LLMs across four tasks: scientific knowledge understanding, mathematical reasoning, creative and functional writing, and code generation and explanation. Experimental results show that our sample-efficient evaluation method recovers "gold-standard" model rankings with a handful of MAD-selected instructions, reveals respective strengths and weaknesses of each LLM, and offers nuanced insights to guide future LLM development. Code is available at https://github.com/weiji-Feng/MAD-Eval .

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