  The growing number of parameter-efficient adaptations of a base large
language model (LLM) calls for studying whether we can reuse such trained
adapters to improve performance for new tasks. We study how to best build a
library of adapters given multi-task data and devise techniques for both
zero-shot and supervised task generalization through routing in such library.
We benchmark existing approaches to build this library and introduce
model-based clustering, MBC, a method that groups tasks based on the similarity
of their adapter parameters, indirectly optimizing for transfer across the
multi-task dataset. To re-use the library, we present a novel zero-shot routing
mechanism, Arrow, which enables dynamic selection of the most relevant adapters
for new inputs without the need for retraining. We experiment with several
LLMs, such as Phi-2 and Mistral, on a wide array of held-out tasks, verifying
that MBC-based adapters and Arrow routing lead to superior generalization to
new tasks. We make steps towards creating modular, adaptable LLMs that can
match or outperform traditional joint training.
