Researchers Introduce Proxy-Tuning: An Efficient Alternative to Finetuning Large Language Models

Researchers from the University of Washington and the Allen Institute for AI have set a new precedent in the work of fine-tuning LLMs. The study, led by Alisa Liu, Xiaochuang Han, Yizhong Wang, Yulia Tsvetkov, Yejin Choi, and Noah A. Smith, introduces a concept known as “proxy-tuning,” a method that promises to streamline the adaptation of large pretrained LMs efficiently.

Traditionally, large language models like GPT and BERT have required extensive resources for fine-tuning to meet specific needs or …

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