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_keywords: 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