Sample-Efficient Human Evaluation of Large Language Models via Maximum
  Discrepancy Competition
Kehua Feng Keyan Ding Kede Ma Zhihua Wang Qiang Zhang Huajun Chen
The past years have witnessed a proliferation of large language models
(LLMs). Yet, automated and unbiased evaluation of LLMs is challenging due to
the inaccuracy of standard metrics in reflecting human preferences and the
inefficiency in sampling informative and diverse test examples. While human
evaluation remains the gold standard, it is expensive and time-consuming,
especially when dealing with a large number of testing samples. To address this
problem, we propose a sample-efficient human evaluation method based on MAximum
Discrepancy (MAD) competition. MAD automatically selects a small set of
informative and diverse instructions, each adapted to two LLMs, whose responses
are subject to three-alternative forced choice by human subjects. The pairwise
comparison results are then aggregated into a global ranking using the Elo
rating system. We select eight representative LLMs and compare them in terms of
four skills: knowledge understanding, mathematical reasoning, writing, and
coding. Experimental results show that the proposed method achieves a reliable
and sensible ranking of LLMs' capabilities, identifies their relative strengths
and weaknesses, and offers valuable insights for further LLM advancement.