主动选择有效样本，提升小样本文本分类效果
Active Few-Shot Learning for Text Classification
- 基于主动学习筛选未标注数据中最有价值的样本作为支持集
- 在5个任务上显著提升小样本学习性能，且不依赖特定模型
- 适合数据稀缺场景下的文本分类研究与应用
大语言模型（LLMs）的兴起推动了自然语言处理中少样本学习（FSL）方法的应用，即使在训练数据有限的情况下也能取得可接受的性能。FSL的目标是高效利用少量标注样本进行学习。然而，当选择不当的支持样本时，FSL性能会下降。这一问题源于对有限支持样本的高度依赖，导致即使增加更多支持样本，性能也难以持续提升。为此，我们提出一种基于主动学习的实例选择机制，能从未标注数据池中识别出有效的支持实例，并适用于不同LLMs。在五个任务上的实验表明，该方法能频繁提升FSL性能。代码已开源至GitHub。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。