LLM-adapters: An adapter family for parameter-efficient fine-tuning of large language models

• Main Authors: HU, Zhiqiang, WANG, Lei, LAN, Yihuai, XU, Wanyu, LIM, Ee-peng, BING, Lidong, XU, Xing, PORIA, Soujanya, LEE, Roy Ka-Wei
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Databases and Information Systems; Programming Languages and Compilers
• Online Access: https://ink.library.smu.edu.sg/sis_research/8324; https://ink.library.smu.edu.sg/context/sis_research/article/9327/viewcontent/2304.01933.pdf
• Institution: Singapore Management University

The success of large language models (LLMs), like GPT-4 and ChatGPT, has led to the development of numerous cost-effective and accessible alternatives that are created by finetuning open-access LLMs with task-specific data (e.g., ChatDoctor) or instruction data (e.g., Alpaca). Among the various fine...