Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

V. Lialin, V. Deshpande, A. Rumshisky

Paper link

This paper presents a systematic overview and comparison of parameter-efficient fine-tuning methods covering over 40 papers published between February 2019 and February 2023. These methods aim to resolve the infeasibility and impracticality of fine-tuning large language models by only training a small set of parameters. We provide a taxonomy that covers a broad range of methods and present a detailed method comparison with a specific focus on real-life efficiency and fine-tuning multibillion-scale language models.

PEFT Taxonomy


    title={Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning},
    author={Vladislav Lialin and Vijeta Deshpande and Anna Rumshisky},