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Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning. (arXiv:2205.05638v2 [cs.LG] UPDATED)
Aug. 29, 2022, 1:11 a.m. | Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, Colin Raffel
cs.LG updates on arXiv.org arxiv.org
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 …
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