DLoRA: Distributed Parameter-Efficient Fine-Tuning Solution for Large Language Model

arXiv:2404.05182v1 Announce Type: new
Abstract: To enhance the performance of large language models (LLM) on downstream tasks, one solution is to fine-tune certain LLM parameters and make it better align with the characteristics of the training dataset. This process is commonly known as parameter-efficient fine-tuning (PEFT). Due to the scale of LLM, PEFT operations are usually executed in the public environment (e.g., cloud server). This necessitates the sharing of sensitive user data across public environments, thereby raising potential privacy concerns. …

abstract arxiv cs.ai cs.cl cs.dc cs.lg dataset distributed fine-tuning language language model language models large language large language model large language models llm parameters peft performance process scale solution tasks training type