Moreover, GPT-based zero-shot classification models tend to make independent predictions over test instances, which can be sub-optimal as the instance correlations and the decision boundaries in the target space are ignored. To address these difficulties and limitations, we propose a new approach to zero-shot text classification, namely \ourmodelshort, which leverages the strong generative power of GPT to assist in training a smaller, more adaptable, and efficient sentence encoder classifier with contrastive self-training. Specifically, GenCo applies GPT in two ways: firstly, it generates multiple augmented texts for each input instance to enhance the semantic embedding of the instance and improve the mapping to relevant labels; secondly, it generates augmented texts conditioned on the predicted label during self-training, which makes the generative process tailored to the decision boundaries in the target space. In our experiments, GenCo outperforms previous state-of-the-art methods on multiple benchmark datasets, even when only limited in-domain text data is available.

翻译：此外，基于GPT的零样本分类模型倾向于对测试实例进行独立预测，这可能因忽略实例相关性及目标空间中的决策边界而导致次优结果。为解决这些困难与局限性，我们提出一种新的零样本文本分类方法——\ourmodelshort，该方法利用GPT强大的生成能力，通过对比自训练辅助训练一个更小、更适应性强且高效的句子编码器分类器。具体而言，GenCo通过两种方式应用GPT：首先，对每个输入实例生成多个增强文本以增强其语义嵌入并改进与相关标签的映射；其次，在自训练过程中基于预测标签生成条件增强文本，使生成过程适配目标空间的决策边界。实验表明，即使仅有有限的领域内文本数据可用，GenCo在多个基准数据集上仍优于先前的最优方法。