Few-shot methods for accurate modeling under sparse label-settings have improved significantly. However, the applications of few-shot modeling in natural language processing remain solely in the field of document classification. With recent performance improvements, supervised few-shot methods, combined with a simple topic extraction method pose a significant challenge to unsupervised topic modeling methods. Our research shows that supervised few-shot learning, combined with a simple topic extraction method, can outperform unsupervised topic modeling techniques in terms of generating coherent topics, even when only a few labeled documents per class are used.
@article{arxiv.2212.09422,
title = {Human in the loop: How to effectively create coherent topics by manually labeling only a few documents per class},
author = {Anton Thielmann and Christoph Weisser and Benjamin Säfken},
journal= {arXiv preprint arXiv:2212.09422},
year = {2022}
}