  Despite the success, the process of fine-tuning large-scale PLMs brings
prohibitive adaptation costs. In fact, fine-tuning all the parameters of a
colossal model and retaining separate instances for different tasks are
practically infeasible. This necessitates a new branch of research focusing on
the parameter-efficient adaptation of PLMs, dubbed as delta tuning in this
paper. In contrast with the standard fine-tuning, delta tuning only fine-tunes
a small portion of the model parameters while keeping the rest untouched,
largely reducing both the computation and storage costs. Recent studies have
demonstrated that a series of delta tuning methods with distinct tuned
parameter selection could achieve performance on a par with full-parameter
fine-tuning, suggesting a new promising way of stimulating large-scale PLMs. In
this paper, we first formally describe the problem of delta tuning and then
comprehensively review recent delta tuning approaches. We also propose a
unified categorization criterion that divide existing delta tuning methods into
three groups: addition-based, specification-based, and reparameterization-based
methods. Though initially proposed as an efficient method to steer large
models, we believe that some of the fascinating evidence discovered along with
delta tuning could help further reveal the mechanisms of PLMs and even deep
neural networks. To this end, we discuss the theoretical principles underlying
the effectiveness of delta tuning and propose frameworks to interpret delta
tuning from the perspective of optimization and optimal control, respectively.
Furthermore, we provide a holistic empirical study of representative methods,
where results on over 100 NLP tasks demonstrate a comprehensive performance
comparison of different approaches. The experimental results also cover the
analysis of combinatorial, scaling and transferable properties of delta tuning.
