CombLM: Adapting Black-Box Language Models through Small Fine-Tuned Models

Aitor Ormazabal, Mikel Artetxe, Eneko Agirre


Abstract
Methods for adapting language models (LMs) to new tasks and domains have traditionally assumed white-box access to the model, and work by modifying its parameters. However, this is incompatible with a recent trend in the field, where the highest quality models are only available as black-boxes through inference APIs. Even when the model weights are available, the computational cost of fine-tuning large LMs can be prohibitive for most practitioners. In this work, we present a lightweight method for adapting large LMs to new domains and tasks, assuming no access to their weights or intermediate activations. Our approach fine-tunes a small white-box LM and combines it with the large black-box LM at the probability level through a small network, learned on a small validation set. We validate our approach by adapting a large LM (OPT-30B) to several domains and a downstream task (machine translation), observing improved performance in all cases, of up to 9%, while using a domain expert 23x smaller.
Anthology ID:
2023.emnlp-main.180
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2961–2974
Language:
URL:
https://aclanthology.org/2023.emnlp-main.180
DOI:
10.18653/v1/2023.emnlp-main.180
Bibkey:
Cite (ACL):
Aitor Ormazabal, Mikel Artetxe, and Eneko Agirre. 2023. CombLM: Adapting Black-Box Language Models through Small Fine-Tuned Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2961–2974, Singapore. Association for Computational Linguistics.
Cite (Informal):
CombLM: Adapting Black-Box Language Models through Small Fine-Tuned Models (Ormazabal et al., EMNLP 2023)
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