Moelora: Contrastive Learning Guided Mixture Of Experts On Parameter-efficient Fine-tuning For Large Language Models

Abstract

Fine-tuning is often necessary to enhance the adaptability of Large Language Models (LLM) to downstream tasks. Nonetheless, the process of updating billions of parameters demands significant computational resources and training time, which poses a substantial obstacle to the widespread application of large-scale models in various scenarios. To address this issue, Parameter-Efficient Fine-Tuning (P