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

• URL: http://arxiv.org/abs/2305.16876v1
• Date: Tue, 23 May 2023 06:32:55 GMT
• Title: CombLM: Adapting Black-Box Language Models through Small Fine-Tuned Models
• Authors: Aitor Ormazabal, Mikel Artetxe and Eneko Agirre
• Abstract summary: Methods for adapting language models (LMs) to new tasks and domains have traditionally assumed white-box access to the model. 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.
• Score: 43.28607973774104
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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