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
646c351a31968a60a0237c71
/avatars/d1b3ef3c33671d1cb097bad69e97943b.svg
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
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 .
JudgeBench is a benchmark for evaluating LLM-based judges using objective correctness for challenging tasks like knowledge, reasoning, math, and coding.
LLM-based judges human evaluation JudgeBench challenging response pairs preference labels prompted judges fine-tuned judges multi-agent judges reward models