Zero Shot Text Classification

Zero-shot text classification aims to categorize text into predefined categories without using any labeled examples from those categories during training. Current research heavily utilizes large language models (LLMs), often employing prompting techniques or contrastive learning methods to leverage pre-trained knowledge for effective classification. Focus areas include improving robustness to prompt variations, exploring the effectiveness of smaller, more resource-efficient models, and developing methods to handle unseen or nuanced labels. This field holds significant promise for automating various text processing tasks, reducing the reliance on expensive and time-consuming data annotation, and enabling applications in diverse domains such as spam detection and hate speech identification.