Lunch at 12:30pm, talk at 1pm, in 148 Fitzpatrick

Title: Leveraging Large Language Models to Assist with Nutrition and Dietary Health: Design Implications from a Study with Registered Dietitians

Abstract: Large Language Models (LLMs) have the potential to significantly contribute to the fields of nutrition and dietetics, particularly in generating food product explanations that facilitate informed food selections for individuals. However, the extent to which these models can offer effective and accurate information remains largely unexplored and unverified. This study addresses this gap by examining LLMs’ capabilities and limitations in generating accurate nutrition information. We assess the impact of varying levels of specificity in the prompts used for generating LLM explanations. Using a mixed-method approach, we collaborate with registered dietitians to evaluate the nutrition information generated by the model. From this collaboration, our research proposes a set of design implications to shape the future of using LLMs when producing nuanced dietary information. These design implications, when utilized, may enhance the practicality and efficacy in generating comprehensive explanations of food products to provide customized nutrition information.

Bio: Annalisa Szymanski is a third-year PhD student studying Human-Computer Interaction under the supervision of Dr. Ronald Metoyer. At present, she is engaged in a project dedicated to enhancing food accessibility within food desert communities. Her research is focused on investigating the optimal utilization of AI solutions to harness nutrition data for the generation of personalized food recommendations and for the educational resources that cater to the diverse needs of all users.