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

Understand

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.