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This work presents an unsupervised method for automatically constructing andexpanding topic taxonomies by using instruction-based fine-tuned LLMs (LargeLanguage Models). We apply topic modeling and keyword extraction techniques tocreate initial topic taxonomies and LLMs to post-process the resulting termsand create a hierarchy. To expand an existing taxonomy with new terms, we usezero-shot prompting to find out where to add new nodes, which, to ourknowledge, is the first work to present such an approach to taxonomy tasks. Weuse the resulting taxonomies to assign tags that characterize merchants from aretail bank dataset. To evaluate our work, we asked 12 volunteers to answer atwo-part form in which we first assessed the quality of the taxonomies createdand then the tags assigned to merchants based on that taxonomy. The evaluationrevealed a coherence rate exceeding 90% for the chosen taxonomies, while theaverage coherence for merchant tagging surpassed 80%.

Using Zero-shot Prompting in the Automatic Creation and Expansion of Topic Taxonomies for Tagging Retail Banking Transactions