Using Clustering to Improve the Performance of few-shot Learning

With the rise of pre-trained language models, few-shot learning has experienced significant progress in terms of performance. Nevertheless, there remains considerable scope for improvement. The objective of this study is to improve the efficacy of few-shot learning by introducing an enhanced prompt learning approach that maximizes the utilization of limited supervised information. Furthermore, our approach integrates a deep clustering method to leverage unlabeled data, thereby further enhancing the performance of few-shot learning. Our ongoing experiments have yielded promising results, which pave the way for future advancements in this domain.