Annalisa Szymanski
University of Notre Dame, South Bend, Indiana, United States
Large Language Models (LLMs) have the potential to contribute to the fields of nutrition and dietetics in generating food product explanations that facilitate informed food selections. However, the extent to which these models offer effective and accurate information remains unverified. In collaboration with registered dietitians (RDs), we evaluate the strengths and weaknesses of LLMs in providing accurate and personalized nutrition information. Through a mixed-methods approach, RDs validated GPT-4 outputs at various levels of prompt specificity, which led to the development of design guidelines used to prompt LLMs for nutrition information. We tested these guidelines by creating a GPT prototype, The Food Product Nutrition Assistant, tailored for food product explanations. This prototype was refined and evaluated in focus groups with RDs. We find that the implementation of these dietitian-reviewed template instructions enhance the generation of detailed food product descriptions and tailored nutrition information.
doi.org/10.1145/3613904.3641924
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)