Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling

arXiv:2403.16248v1 Announce Type: new
Abstract: Topic modelling, as a well-established unsupervised technique, has found extensive use in automatically detecting significant topics within a corpus of documents. However, classic topic modelling approaches (e.g., LDA) have certain drawbacks, such as the lack of semantic understanding and the presence of overlapping topics. In this work, we investigate the untapped potential of large language models (LLMs) as an alternative for uncovering the underlying topics within extensive text corpora. To this end, we introduce a …

abstract arxiv cs.cl documents found however language language models large language large language models lda modelling semantic topics type understanding unsupervised