4 accepted papers
In recent years, machine learning (ML) models have been integrated into various applications and products to improve user experience. However, this approach raises significant concerns about the protection of private user data utilized for training the models. One limitation of vanilla privacy metho…
Large language models (LLMs) often leverage adapters, such as low-rank-based adapters, to achieve strong performance on downstream tasks. However, storing a separate adapter for each task significantly increases memory requirements, posing a challenge for resource-constrained environ ments such as m
Parameter-efficient fine-tuning (PEFT) is utilized to adapt large pre-trained machine learning (ML) models to new tasks using a small number of trainable parameters. In particular, Low-Rank Adaptation (LoRA) is one of the prominent PEFT methods. To this end, we introduce a novel method that exploits…
The growing size of large language models (LLMs) requires parameter-efficient fine-tuning (PEFT) methods for their adaptation to new tasks. Existing methods, such as Low-Rank Adaptation (LoRA), typically involve model adaptation by training the PEFT parameters. One open problem required to be solved…