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.

通过研究不同任务的共享参数模型训练器的可重复使用性，我们调研了如何根据多任务数据构建最佳适配器库，并设计了通过库中的路由进行零样本和监督任务泛化的技术。我们对构建适配器库的现有方法进行了基准测试，并引入了基于模型的聚类方法 MBC，通过间接优化跨多任务数据集的传输来分组任务。为了重复使用该库，我们提出了一种新颖的零样本路由机制 Arrow，它能够动态选择最相关的适配器用于新的输入，无需重新训练。我们在多种大型语言模型（LLMs），如 Phi-2 和 Mistral 上进行了实验，针对大量保留任务验证了基于 MBC 的适配器和 Arrow 路由在新任务的泛化性上的卓越表现。我们迈出了创建模块化、适应性强、能够与传统联合训练不相上下甚至超越的 LLMs 的步伐。