  This work presents an unsupervised method for automatically constructing and
expanding topic taxonomies using instruction-based fine-tuned LLMs (Large
Language Models). We apply topic modeling and keyword extraction techniques to
create initial topic taxonomies and LLMs to post-process the resulting terms
and create a hierarchy. To expand an existing taxonomy with new terms, we use
zero-shot prompting to find out where to add new nodes, which, to our
knowledge, is the first work to present such an approach to taxonomy tasks. We
use the resulting taxonomies to assign tags that characterize merchants from a
retail bank dataset. To evaluate our work, we asked 12 volunteers to answer a
two-part form in which we first assessed the quality of the taxonomies created
and then the tags assigned to merchants based on that taxonomy. The evaluation
revealed a coherence rate exceeding 90% for the chosen taxonomies. The
taxonomies' expansion with LLMs also showed exciting results for parent node
prediction, with an f1-score above 70% in our taxonomies.
