SIBO : A simple booster for parameter-efficient fine-tuning

• Main Authors: WEN, Zhihao, ZHANG, Jie, FANG, Yuan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Large language models; LLMs; Parameter-efficient fine-tuning; Artificial Intelligence and Robotics; Computer Sciences
• Online Access: https://ink.library.smu.edu.sg/sis_research/9624; https://ink.library.smu.edu.sg/context/sis_research/article/10624/viewcontent/ACL24Findings_SIBO__1_.pdf
• Institution: Singapore Management University

Fine-tuning all parameters of large language models (LLMs) necessitates substantial computational power and extended time. Latest advancements in parameter-efficient fine-tuning (PEFT) techniques, such as Adapter tuning and LoRA, allow for adjustments to only a minor fraction of the parameters of th...