title: Foundational Autoraters: Taming Large Language Models for Better
    Automatic Evaluation
summary: As large language models (LLMs) advance, it becomes more challenging to
  reliably evaluate their output due to the high costs of human evaluation. To
  make progress towards better LLM autoraters, we introduce FLAMe, a family of
  Foundational Large Autorater Models. FLAMe is trained on our large and diverse
  collection of 100+ quality assessment tasks comprising 5M+ human judgments,
  curated and standardized using publicly released human evaluations from
  previous research. FLAMe significantly improves generalization to a wide
  variety of held-out tasks, outperforming LLMs trained on proprietary data like
  GPT-4 and Claude-3 on many tasks. We show that FLAMe can also serve as a
  powerful starting point for further downstream fine-tuning, using reward
  modeling evaluation as a case study (FLAMe-RM). Notably, on RewardBench, our
  FLAMe-RM-24B model (with an accuracy of 87.8%) is the top-performing generative
  model trained exclusively on permissively licensed data, outperforming both
  GPT-4-0125 (85.9%) and GPT-4o (84.7%). Additionally, we explore a more
  computationally efficient approach using a novel tail-patch fine-tuning
  strategy to optimize our FLAMe multitask mixture for reward modeling evaluation
  (FLAMe-Opt-RM), offering competitive RewardBench performance while requiring
  approximately 25x less training datapoints. Overall, our FLAMe variants
  outperform all popular proprietary LLM-as-a-Judge models we consider across 8
  out of 12 autorater evaluation benchmarks, encompassing 53 quality assessment
  tasks, including RewardBench and LLM-AggreFact. Finally, our analysis reveals
  that FLAMe is significantly less biased than these LLM-as-a-Judge models on the
  CoBBLEr autorater bias benchmark, while effectively identifying high-quality
  responses for code generation.
paper_authors._id: 66960e40c73fcf7a04fb21cf
paper_authors.name: Tu Vu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-16T20:15:21.585Z
paper_authors._id: 636828a644e19ccad213063a
paper_authors.avatarUrl: /avatars/e1551d4ee9ca94018103af3bf1249cf2.svg
paper_authors.fullname: Tu Vu
paper_authors.type: user
paper_authors.user: tuvu
paper_authors._id: 66960e40c73fcf7a04fb21d0
paper_authors.name: Kalpesh Krishna
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-18T09:07:55.063Z
paper_authors._id: 62b9f1034dde8074b78cc6f2
paper_authors.avatarUrl: /avatars/2bde72c6b8baee2b31f318b3010e483a.svg
paper_authors.fullname: Kalpesh Krishna
paper_authors.type: user
paper_authors.user: kalpeshk2011
paper_authors._id: 66960e40c73fcf7a04fb21d1
paper_authors.name: Salaheddin Alzubi
paper_authors._id: 66960e40c73fcf7a04fb21d2
paper_authors.name: Chris Tar
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-26T08:26:16.611Z
paper_authors._id: 623e5bd5d10f369bb5f2d392
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/623e5bd5d10f369bb5f2d392/u3nib56InZsTxPrdYPAd1.jpeg
paper_authors.fullname: Chris Tar
paper_authors.type: user
paper_authors.user: ctar
paper_authors._id: 66960e40c73fcf7a04fb21d3
paper_authors.name: Manaal Faruqui
paper_authors._id: 66960e40c73fcf7a04fb21d4
paper_authors.name: Yun-Hsuan Sung
paper_title: Foundational Autoraters: Taming Large Language Models for Better
    Automatic Evaluation
paper_summary: As large language models (LLMs) advance, it becomes more challenging to
  reliably evaluate their output due to the high costs of human evaluation. To
  make progress towards better LLM autoraters, we introduce FLAMe, a family of
  Foundational Large Autorater Models. FLAMe is trained on our large and diverse
  collection of 100+ quality assessment tasks comprising 5M+ human judgments,
  curated and standardized using publicly released human evaluations from
  previous research. FLAMe significantly improves generalization to a wide
  variety of held-out tasks, outperforming LLMs trained on proprietary data like
  GPT-4 and Claude-3 on many tasks. We show that FLAMe can also serve as a
  powerful starting point for further downstream fine-tuning, using reward
  modeling evaluation as a case study (FLAMe-RM). Notably, on RewardBench, our
  FLAMe-RM-24B model (with an accuracy of 87.8%) is the top-performing generative
  model trained exclusively on permissively licensed data, outperforming both
  GPT-4-0125 (85.9%) and GPT-4o (84.7%). Additionally, we explore a more
  computationally efficient approach using a novel tail-patch fine-tuning
  strategy to optimize our FLAMe multitask mixture for reward modeling evaluation
  (FLAMe-Opt-RM), offering competitive RewardBench performance while requiring
  approximately 25x less training datapoints. Overall, our FLAMe variants
  outperform all popular proprietary LLM-as-a-Judge models we consider across 8
  out of 12 autorater evaluation benchmarks, encompassing 53 quality assessment
  tasks, including RewardBench and LLM-AggreFact. Finally, our analysis reveals
  that FLAMe is significantly less biased than these LLM-as-a-Judge models on the
  CoBBLEr autorater bias benchmark, while effectively identifying high-quality
  responses for code generation.
paper_ai_keywords: FLAMe Foundational Large Autorater Models quality assessment tasks human judgments generalization LLM autoraters LLAMs trained on proprietary data GPT-4 Claude-3 reward modeling evaluation FLAMe-RM RewardBench FLAMe-RM-24B permissively licensed data tail-patch fine-tuning FLAMe multitask mixture FLAMe-Opt-RM LLM-as-a-Judge models autorater bias benchmark CoBBLEr code generation LLM-AggreFact