Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning. (arXiv:2205.05638v2 [cs.LG] UPDATED)

Few-shot in-context learning (ICL) enables pre-trained language models to
perform a previously-unseen task without any gradient-based training by feeding
a small number of training examples as part of the input. ICL incurs
substantial computational, memory, and storage costs because it involves
processing all of the training examples every time a prediction is made.
Parameter-efficient fine-tuning (PEFT) (e.g. adapter modules, prompt tuning,
sparse update methods, etc.) offers an alternative paradigm where a small set
of parameters are trained to enable a …

arxiv context fine-tuning learning lg