Improving Few-Shot Performance of Language Models via Nearest Neighbor Calibration
This addresses the problem of unstable few-shot learning for users of language models, offering an incremental improvement over existing in-context learning techniques.
The paper tackles the sensitivity of in-context learning in pre-trained language models to prompt variations by proposing a nearest-neighbor calibration framework, which significantly improves few-shot text classification performance and achieves comparable results to state-of-the-art tuning-based methods in some sentiment analysis tasks.
Pre-trained language models (PLMs) have exhibited remarkable few-shot learning capabilities when provided a few examples in a natural language prompt as demonstrations of test instances, i.e., in-context learning. However, the performance of in-context learning is susceptible to the choice of prompt format, training examples and the ordering of the training examples. In this paper, we propose a novel nearest-neighbor calibration framework for in-context learning to ease this issue. It is inspired by a phenomenon that the in-context learning paradigm produces incorrect labels when inferring training instances, which provides a useful supervised signal to calibrate predictions. Thus, our method directly augments the predictions with a $k$-nearest-neighbor ($k$NN) classifier over a datastore of cached few-shot instance representations obtained by PLMs and their corresponding labels. Then adaptive neighbor selection and feature regularization modules are introduced to make full use of a few support instances to reduce the $k$NN retrieval noise. Experiments on various few-shot text classification tasks demonstrate that our method significantly improves in-context learning, while even achieving comparable performance with state-of-the-art tuning-based approaches in some sentiment analysis tasks.