Foundational Autoraters: Taming Large Language Models for Better Automatic Evaluation

Tu Vu, Kalpesh Krishna, Salaheddin Alzubi, Chris Tar, Manaal Faruqui, Yun-Hsuan Sung


Abstract
As large language models (LLMs) evolve, evaluating their output reliably becomes increasingly difficult due to the high cost of human evaluation. To address this, we introduce FLAMe, a family of Foundational Large Autorater Models. FLAMe is trained on a diverse set of over 100 quality assessment tasks, incorporating 5M+ human judgments curated from publicly released human evaluations. FLAMe outperforms models like GPT-4 and Claude-3 on various held-out tasks, and serves as a powerful starting point for fine-tuning, as shown in our reward model evaluation case study (FLAMe-RM). On Reward-Bench, FLAMe-RM-24B achieves 87.8% accuracy, surpassing GPT-4-0125 (85.9%) and GPT-4o (84.7%). Additionally, we introduce FLAMe-Opt-RM, an efficient tail-patch fine-tuning approach that offers competitive RewardBench performance using 25×fewer training datapoints. Our FLAMe variants outperform popular proprietary LLM-as-a-Judge models on 8 of 12 autorater benchmarks, covering 53 quality assessment tasks, including RewardBench and LLM-AggreFact. Finally, our analysis shows that FLAMe is significantly less biased than other LLM-as-a-Judge models on the CoBBLEr autorater bias benchmark.
Anthology ID:
2024.emnlp-main.949
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
17086–17105
Language:
URL:
https://aclanthology.org/2024.emnlp-main.949
DOI:
10.18653/v1/2024.emnlp-main.949
Bibkey:
Cite (ACL):
Tu Vu, Kalpesh Krishna, Salaheddin Alzubi, Chris Tar, Manaal Faruqui, and Yun-Hsuan Sung. 2024. Foundational Autoraters: Taming Large Language Models for Better Automatic Evaluation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 17086–17105, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Foundational Autoraters: Taming Large Language Models for Better Automatic Evaluation (Vu et al., EMNLP 2024)
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PDF:
https://aclanthology.org/2024.emnlp-main.949.pdf