JudgeBench: A Benchmark for Evaluating LLM-based Judges
Authors: name: Sijun Tan, name: Siyuan Zhuang, name: Kyle Montgomery, name: William Y. Tang, name: Alejandro Cuadron, name: Chenguang Wang, name: Raluca Ada Popa, name: Ion Stoica

LLM-based judges have emerged as a scalable alternative to human evaluation
and are increasingly used to assess, compare, and improve models. However, the
reliability of LLM-based judges themselves is rarely scrutinized. As LLMs
become more advanced, their responses grow more sophisticated, requiring
stronger judges to evaluate them. Existing benchmarks primarily focus on a
judge's alignment with human preferences, but often fail to account for more
challenging tasks where crowdsourced human preference is a poor indicator of
factual and logical correctness. To address this, we propose a novel evaluation
framework to objectively evaluate LLM-based judges. Based on this framework, we
propose JudgeBench, a benchmark for evaluating LLM-based judges on challenging
response pairs spanning knowledge, reasoning, math, and coding. JudgeBench
leverages a novel pipeline for converting existing difficult datasets into
challenging response pairs with preference labels reflecting objective
correctness. Our comprehensive evaluation on a collection of prompted judges,
fine-tuned judges, multi-agent judges, and reward models shows that JudgeBench
poses a significantly greater challenge than previous benchmarks, with many
strong models (e.g., GPT-4o) performing just slightly better than random
guessing. Overall, JudgeBench offers a reliable platform for assessing
increasingly advanced LLM-based judges. Data and code are available at
https://github.com/ScalerLab/JudgeBench .