  Large pretrained language models are widely used in downstream NLP tasks via
task-specific fine-tuning, but such procedures can be costly. Recently,
Parameter-Efficient Fine-Tuning (PEFT) methods have achieved strong task
performance while updating much fewer parameters than full model fine-tuning
(FFT). However, it is non-trivial to make informed design choices on the PEFT
configurations, such as their architecture, the number of tunable parameters,
and even the layers in which the PEFT modules are inserted. Consequently, it is
highly likely that the current, manually designed configurations are suboptimal
in terms of their performance-efficiency trade-off. Inspired by advances in
neural architecture search, we propose AutoPEFT for automatic PEFT
configuration selection: we first design an expressive configuration search
space with multiple representative PEFT modules as building blocks. Using
multi-objective Bayesian optimisation in a low-cost setup, we then discover a
Pareto-optimal set of configurations with strong performance-cost trade-offs
across different numbers of parameters that are also highly transferable across
different tasks. Empirically, on GLUE and SuperGLUE tasks, we show that
AutoPEFT-discovered configurations significantly outperform existing PEFT
methods and are on par or better than FFT without incurring substantial
training efficiency costs.
