Abstract
Large language models demonstrate an emergent ability to learn a new task from a small number of input-output demonstrations, referred to as in-context few-shot learning. However, recent work shows that in such settings, models mainly learn to mimic the new task distribution, instead of the mechanics of the new task. We argue that the commonly-used evaluation settings of few-shot models utilizing a random selection of in-context demonstrations is not able to disentangle models' ability to learn new skills from demonstrations, as most of the such-selected demonstrations are not informative for prediction beyond exposing the new task's input and output distribution. Therefore, we introduce an evaluation technique that disentangles few-shot learners' gain from in-context learning by picking the demonstrations sharing a specific, informative concept with the predicted sample, in addition to the performance reached by mainly non-informative samples. We find that regardless of the model size, existing few-shot learners are not able to benefit from observing such informative concepts in demonstrations. We also find that such ability may not be obtained trivially by exposing the informative demonstrations in the training process, leaving the challenge of training true in-context learners open.
Abstract (translated)
URL
https://arxiv.org/abs/2212.01692
