Towards Modular LLMs by Building and Reusing a Library of LoRAs

• Main Authors: Ostapenko, Oleksiy, Su, Zhan, Ponti, Edoardo Maria, Charlin, Laurent, Roux, Nicolas Le, Pereira, Matheus, Caccia, Lucas, Sordoni, Alessandro
• Format: Journal Article
• Language: English
• Published: 17.05.2024
• Subjects: Computer Science - Computation and Language; Computer Science - Learning
• DOI: 10.48550/arxiv.2405.11157

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.