Active learning (AL) aims to optimize model training and reduce annotation costs by selecting the most informative samples for labeling. Typically, AL methods rely on the empirical distribution of labeled data to define the decision boundary and perform uncertainty or diversity estimation, subsequently identifying potential high-quality samples. In few-shot scenarios, the empirical distribution often diverges significantly from the target distribution, causing the decision boundary to shift away from its optimal position. However, existing methods overlook the role of unlabeled samples in enhancing the empirical distribution to better align with the target distribution, resulting in a suboptimal decision boundary and the selection of samples that inadequately represent the target distribution. To address this, we propose a hybrid AL framework, termed \textbf{PromptAL} (Sample-Aware Dynamic Soft \textbf{Prompts} for Few-Shot \textbf{A}ctive \textbf{L}earning). This framework accounts for the contribution of each unlabeled data point in aligning the current empirical distribution with the target distribution, thereby optimizing the decision boundary. Specifically, PromptAL first leverages unlabeled data to construct sample-aware dynamic soft prompts that adjust the model's predictive distribution and decision boundary. Subsequently, based on the adjusted decision boundary, it integrates uncertainty estimation with both global and local diversity to select high-quality samples that more accurately represent the target distribution. Experimental results on six in-domain and three out-of-domain datasets show that PromptAL achieves superior performance over nine baselines. Our codebase is openly accessible.

翻译：主动学习（Active Learning, AL）旨在通过选择最具信息量的样本进行标注，以优化模型训练并降低标注成本。通常，AL方法依赖于标注数据的经验分布来定义决策边界，并进行不确定性或多样性估计，进而识别潜在的高质量样本。在少样本场景下，经验分布往往与目标分布存在显著偏差，导致决策边界偏离其最优位置。然而，现有方法忽视了未标注样本在增强经验分布以更好地对齐目标分布方面的作用，从而导致决策边界次优，并选择了不足以代表目标分布的样本。为解决这一问题，我们提出了一种混合主动学习框架，称为 **PromptAL**（面向少样本主动学习的样本感知动态软提示）。该框架考虑了每个未标注数据点在使当前经验分布与目标分布对齐过程中的贡献，从而优化决策边界。具体而言，PromptAL首先利用未标注数据构建样本感知的动态软提示，以调整模型的预测分布和决策边界。随后，基于调整后的决策边界，它将不确定性估计与全局及局部多样性相结合，以选择能更准确代表目标分布的高质量样本。在六个域内数据集和三个域外数据集上的实验结果表明，PromptAL在九个基线方法中取得了更优的性能。我们的代码库已公开可访问。