Foundational Autoraters: Taming Large Language Models for Better
  Automatic Evaluation
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.
66960e40c73fcf7a04fb21cf
Tu Vu
claimed_verified
2024-07-16T20:15:21.585Z
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Tu Vu
user
tuvu
66960e40c73fcf7a04fb21d0
Kalpesh Krishna
claimed_verified
2024-07-18T09:07:55.063Z
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/avatars/2bde72c6b8baee2b31f318b3010e483a.svg
Kalpesh Krishna
user
kalpeshk2011
66960e40c73fcf7a04fb21d1
Salaheddin Alzubi
66960e40c73fcf7a04fb21d2
Chris Tar
claimed_verified
2024-09-26T08:26:16.611Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/623e5bd5d10f369bb5f2d392/u3nib56InZsTxPrdYPAd1.jpeg
Chris Tar
user
ctar
66960e40c73fcf7a04fb21d3
Manaal Faruqui
66960e40c73fcf7a04fb21d4
Yun-Hsuan Sung
Foundational Autoraters: Taming Large Language Models for Better
  Automatic Evaluation
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.
FLAMe, a family of Foundational Large Autorater Models trained on publicly licensed data, outperforms proprietary LLMs across multiple quality assessment tasks and demonstrates reduced bias.
Foundational Large Autorater Models FLAMe quality assessment tasks human judgments large language models (LLMs) generalization downstream fine-tuning reward modeling evaluation FLAMe-RM RewardBench FLAMe-RM-24B tail-patch fine-tuning FLAMe-Opt-RM autorater evaluation benchmarks CoBBLEr autorater bias benchmark code generation