Towards Modular LLMs by Building and Reusing a Library of LoRAs
<|reference_start|>Towards Modular LLMs by Building and Reusing a Library of LoRAs: 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.<|reference_end|>
@article{ostapenko2024towards,
    title={Towards Modular LLMs by Building and Reusing a Library of LoRAs},
    author={Oleksiy Ostapenko, Zhan Su, Edoardo Maria Ponti, Laurent Charlin,
  Nicolas Le Roux, Matheus Pereira, Lucas Caccia, Alessandro Sordoni},
    journal={arXiv preprint arXiv:2405.11157},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11157},
    primaryClass={cs.LG cs.CL}
}