Abstract
The rise of Large Language Models (LLMs) has boosted the use of Few-Shot Learning (FSL) methods in natural language processing, achieving acceptable performance even when working with limited training data. The goal of FSL is to effectively utilize a small number of annotated samples in the learning process. However, the performance of FSL suffers when unsuitable support samples are chosen. This problem arises due to the heavy reliance on a limited number of support samples, which hampers consistent performance improvement even when more support samples are added. To address this challenge, we propose an active learning-based instance selection mechanism that identifies effective support instances from the unlabeled pool and can work with different LLMs. Our experiments on five tasks show that our method frequently improves the performance of FSL. We make our implementation available on GitHub.
Abstract (translated)
大规模语言模型（LLMs）的兴起促进了在自然语言处理中使用少量样本学习（FSL）方法的应用，即使在数据量有限的情况下也能达到令人接受的效果。FSL的目标是有效利用少量标注样本进行学习过程。然而，当选择不合适的支持样本来训练时，FSL的表现会受到影响。这一问题源于对少量支持样本的过度依赖，这导致即便增加更多的支持样本也无法持续提升性能表现。为解决这一挑战，我们提出了一种基于主动学习的实例选择机制，该机制可以从未标记的数据池中识别出有效支持实例，并且可以与不同的LLMs协同工作。我们在五个任务上的实验表明，我们的方法能够显著提高FSL的表现。我们将代码开源在GitHub上。
URL
https://arxiv.org/abs/2502.18782
