title: JudgeBench: A Benchmark for Evaluating LLM-based Judges
summary: 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 .
paper_authors._id: 67113ed43a90c03ce46e7f31
paper_authors.name: Sijun Tan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-21T07:48:30.999Z
paper_authors._id: 652867b09903f7a1c9f7cbf1
paper_authors.avatarUrl: /avatars/1d1d3665328943ccdbd6a25ce03f2f10.svg
paper_authors.fullname: Sijun Tan
paper_authors.type: user
paper_authors.user: sijuntan
paper_authors._id: 67113ed43a90c03ce46e7f32
paper_authors.name: Siyuan Zhuang
paper_authors._id: 67113ed43a90c03ce46e7f33
paper_authors.name: Kyle Montgomery
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-10-17T18:01:01.752Z
paper_authors._id: 646c351a31968a60a0237c71
paper_authors.avatarUrl: /avatars/d1b3ef3c33671d1cb097bad69e97943b.svg
paper_authors.fullname: Kyle Montgomery
paper_authors.type: user
paper_authors.user: kylemontgomery
paper_authors._id: 67113ed43a90c03ce46e7f34
paper_authors.name: William Y. Tang
paper_authors._id: 67113ed43a90c03ce46e7f35
paper_authors.name: Alejandro Cuadron
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-21T07:48:29.128Z
paper_authors._id: 652a656d1a3250bbfe3bb92d
paper_authors.avatarUrl: /avatars/a1c25150d55c493edd9a7f81287fc449.svg
paper_authors.fullname: Alejandro Cuadron Lafuente
paper_authors.type: user
paper_authors.user: AlexCuadron
paper_authors._id: 67113ed43a90c03ce46e7f36
paper_authors.name: Chenguang Wang
paper_authors._id: 67113ed43a90c03ce46e7f37
paper_authors.name: Raluca Ada Popa
paper_authors._id: 67113ed43a90c03ce46e7f38
paper_authors.name: Ion Stoica
paper_title: JudgeBench: A Benchmark for Evaluating LLM-based Judges
paper_summary: 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 .
paper_ai_summary: JudgeBench is a benchmark for evaluating LLM-based judges using objective correctness for challenging tasks like knowledge, reasoning, math, and coding.
paper_ai_keywords: LLM-based judges human evaluation JudgeBench challenging response pairs preference labels prompted judges fine-tuned judges multi-agent judges reward models