Analysis reveals potential for enhancing food technology taxonomies with large language models, suggesting new prompting techniques could improve outcomes.
We investigate the utility of Large Language Models for automated taxonomy generation and completion specifically applied to taxonomies from the food technology industry. We explore the extent to which taxonomies can be completed from a seed taxonomy or generated without a seed from a set of known concepts, in an iterative fashion using recent prompting techniques. Experiments on five taxonomies using an open-source LLM (Llama-3), while promising, point to the difficulty of correctly placing inner nodes.
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Wullschleger et al. (2025) studied this question.
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