Offering a promising solution to the scalability challenges associated with
human evaluation, the LLM-as-a-judge paradigm is rapidly gaining traction as an
approach to evaluating large language models (LLMs). However, there are still
many open questions about the strengths and weaknesses of this paradigm, and
what potential biases it may hold. In this paper, we present a comprehensive
study of the performance of various LLMs acting as judges. We leverage TriviaQA
as a benchmark for assessing objective knowledge reasoning of LLMs and evaluate
them alongside human annotations which we found to have a high inter-annotator
agreement. Our study includes 9 judge models and 9 exam taker models -- both
base and instruction-tuned. We assess the judge model's alignment across
different model sizes, families, and judge prompts. Among other results, our
research rediscovers the importance of using Cohen's kappa as a metric of
alignment as opposed to simple percent agreement, showing that judges with high
percent agreement can still assign vastly different scores. We find that both
Llama-3 70B and GPT-4 Turbo have an excellent alignment with humans, but in
terms of ranking exam taker models, they are outperformed by both JudgeLM-7B
and the lexical judge Contains, which have up to 34 points lower human
alignment. Through error analysis and various other studies, including the
effects of instruction length and leniency bias, we hope to provide valuable
lessons for using LLMs as judges in the future.

这篇论文通过对多种语言模型作为判断者的性能进行全面研究，发现了使用 Cohen 的 kappa 作为测度对齐度的重要性，并比较了不同模型之间的判断数据；该研究发现 Llama-3 70B 和 GPT-4 Turbo 语言模型的表现优于人类，然而在排名考生模型方面，JudgeLM-7B 和词汇判断器 Contains 比人类的对齐度低多达 34 分。通过错误分析和其他研究，包括指导长度和仁慈偏见的影响，该论文为今后在判断者角色上使用语言模型提供了宝贵的经验教训。