title: JudgeLM: Fine-tuned Large Language Models are Scalable Judges
summary: Evaluating Large Language Models (LLMs) in open-ended scenarios is
  challenging because existing benchmarks and metrics can not measure them
  comprehensively. To address this problem, we propose to fine-tune LLMs as
  scalable judges (JudgeLM) to evaluate LLMs efficiently and effectively in
  open-ended benchmarks. We first propose a comprehensive, large-scale,
  high-quality dataset containing task seeds, LLMs-generated answers, and
  GPT-4-generated judgments for fine-tuning high-performance judges, as well as a
  new benchmark for evaluating the judges. We train JudgeLM at different scales
  from 7B, 13B, to 33B parameters, and conduct a systematic analysis of its
  capabilities and behaviors. We then analyze the key biases in fine-tuning LLM
  as a judge and consider them as position bias, knowledge bias, and format bias.
  To address these issues, JudgeLM introduces a bag of techniques including swap
  augmentation, reference support, and reference drop, which clearly enhance the
  judge's performance. JudgeLM obtains the state-of-the-art judge performance on
  both the existing PandaLM benchmark and our proposed new benchmark. Our JudgeLM
  is efficient and the JudgeLM-7B only needs 3 minutes to judge 5K samples with 8
  A100 GPUs. JudgeLM obtains high agreement with the teacher judge, achieving an
  agreement exceeding 90% that even surpasses human-to-human agreement. JudgeLM
  also demonstrates extended capabilities in being judges of the single answer,
  multimodal models, multiple answers, and multi-turn chat.
paper_authors._id: 653b158f32bd4db35d88c1e9
paper_authors.name: Lianghui Zhu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-10-27T09:11:45.044Z
paper_authors._id: 643632c6a4bd75c62cc2d3b4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/643632c6a4bd75c62cc2d3b4/rCqEb3_w54-QH2QSP2JhM.jpeg
paper_authors.fullname: Lianghui Zhu
paper_authors.type: user
paper_authors.user: LianghuiZhu
paper_authors._id: 653b158f32bd4db35d88c1ea
paper_authors.name: Xinggang Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-10-27T09:14:30.494Z
paper_authors._id: 62600de6d47e3dbae32ce1ce
paper_authors.avatarUrl: /avatars/a536417cfec6e10ac415091bd1829426.svg
paper_authors.fullname: Xinggang Wang
paper_authors.type: user
paper_authors.user: xinggangw
paper_authors._id: 653b158f32bd4db35d88c1eb
paper_authors.name: Xinlong Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-10-27T09:14:38.360Z
paper_authors._id: 63ca558304c979828311c5a5
paper_authors.avatarUrl: /avatars/2a439d79fba2f987cabe780d10c94d25.svg
paper_authors.fullname: Xinlong Wang
paper_authors.type: user
paper_authors.user: xinlongwang
paper_title: JudgeLM: Fine-tuned Large Language Models are Scalable Judges
paper_summary: Evaluating Large Language Models (LLMs) in open-ended scenarios is
  challenging because existing benchmarks and metrics can not measure them
  comprehensively. To address this problem, we propose to fine-tune LLMs as
  scalable judges (JudgeLM) to evaluate LLMs efficiently and effectively in
  open-ended benchmarks. We first propose a comprehensive, large-scale,
  high-quality dataset containing task seeds, LLMs-generated answers, and
  GPT-4-generated judgments for fine-tuning high-performance judges, as well as a
  new benchmark for evaluating the judges. We train JudgeLM at different scales
  from 7B, 13B, to 33B parameters, and conduct a systematic analysis of its
  capabilities and behaviors. We then analyze the key biases in fine-tuning LLM
  as a judge and consider them as position bias, knowledge bias, and format bias.
  To address these issues, JudgeLM introduces a bag of techniques including swap
  augmentation, reference support, and reference drop, which clearly enhance the
  judge's performance. JudgeLM obtains the state-of-the-art judge performance on
  both the existing PandaLM benchmark and our proposed new benchmark. Our JudgeLM
  is efficient and the JudgeLM-7B only needs 3 minutes to judge 5K samples with 8
  A100 GPUs. JudgeLM obtains high agreement with the teacher judge, achieving an
  agreement exceeding 90% that even surpasses human-to-human agreement. JudgeLM
  also demonstrates extended capabilities in being judges of the single answer,
  multimodal models, multiple answers, and multi-turn chat.
paper_ai_keywords: Large Language Models (LLMs) open-ended visuals benchmarks metrics scalable judges (JudgeLM) fine-tuning task seeds LLLM-generated answers GPT-4-generated answers open-ended benchmarks high-performance judges comprehensive dataset new benchmark parameter scales (7B, 13B, 33B) system capabilities and behaviors position bias knowledge bias reference augmentation reference support reference drop state-of-the-art judge performance PandaLM benchmark efficient judging A100 GPUs high agreement teacher judges human-to-human closed evaluation multimodal models multi-turn chat