  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.
