  Taxonomies represent hierarchical relations between entities, frequently
applied in various software modeling and natural language processing (NLP)
activities. They are typically subject to a set of structural constraints
restricting their content. However, manual taxonomy construction can be
time-consuming, incomplete, and costly to maintain. Recent studies of large
language models (LLMs) have demonstrated that appropriate user inputs (called
prompting) can effectively guide LLMs, such as GPT-3, in diverse NLP tasks
without explicit (re-)training. However, existing approaches for automated
taxonomy construction typically involve fine-tuning a language model by
adjusting model parameters. In this paper, we present a general framework for
taxonomy construction that takes into account structural constraints. We
subsequently conduct a systematic comparison between the prompting and
fine-tuning approaches performed on a hypernym taxonomy and a novel computer
science taxonomy dataset. Our result reveals the following: (1) Even without
explicit training on the dataset, the prompting approach outperforms
fine-tuning-based approaches. Moreover, the performance gap between prompting
and fine-tuning widens when the training dataset is small. However, (2)
taxonomies generated by the fine-tuning approach can be easily post-processed
to satisfy all the constraints, whereas handling violations of the taxonomies
produced by the prompting approach can be challenging. These evaluation
findings provide guidance on selecting the appropriate method for taxonomy
construction and highlight potential enhancements for both approaches.
