JudgeBench: A Benchmark for Evaluating LLM-based Judges
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 .
67113ed43a90c03ce46e7f31
Sijun Tan
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2024-10-21T07:48:30.999Z
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/avatars/1d1d3665328943ccdbd6a25ce03f2f10.svg
Sijun Tan
user
sijuntan
67113ed43a90c03ce46e7f32
Siyuan Zhuang
67113ed43a90c03ce46e7f33
Kyle Montgomery
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2024-10-17T18:01:01.752Z
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Kyle Montgomery
user
kylemontgomery
67113ed43a90c03ce46e7f34
William Y. Tang
67113ed43a90c03ce46e7f35
Alejandro Cuadron
claimed_verified
2024-10-21T07:48:29.128Z
652a656d1a3250bbfe3bb92d
/avatars/a1c25150d55c493edd9a7f81287fc449.svg
Alejandro Cuadron Lafuente
user
AlexCuadron
67113ed43a90c03ce46e7f36
Chenguang Wang
67113ed43a90c03ce46e7f37
Raluca Ada Popa
67113ed43a90c03ce46e7f38
Ion Stoica
JudgeBench: A Benchmark for Evaluating LLM-based Judges
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 .
LLM-based judges Real human evaluation scalable alternative advanced LLMs responses sophisticated frameworks judge's alignment human preferences factual and logical correctness evaluation framework JudgeBench challenging response pairs knowledge reasoning math coding pipeline difficult datasets prompt judges fine-tuned judges multi-agent judges reward models random guessing reliable platform