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 model to perform the new task.
In this paper, we rigorously compare few-shot ICL and PEFT and demonstrate that the latter offers better accuracy as well as dramatically lower computational costs.
Along the way, we introduce a new PEFT method called (IA) 3that scales activations by learned vectors, attaining stronger performance while only introducing a relatively tiny amount of new parameters.
We also propose a simple recipe based on the T0 modelcalled T-Few that can be applied to new tasks without task-specific tuning or modifications.
We validate the effectiveness of T-Few on completely unseen tasks by applying it to the RAFT benchmark, attaining super-human performance for the first time and outperforming the state-of-the-art by 6% absolute.
All of the code used in our experiments is publicly available.111https://github.com/r-three/t-few