Year: 2026
Type: conference-paper
Abstract: People intuitively ask natural language dialogue systems for advice on nutrition and dietary guidelines, but systems based on prompted text generation are susceptible to fabricating details, which could be hazardous to non-specialist users.The FoodBench-QA shared task grounds answers in knowledge bases with linked ontologies, in order to evaluate and mitigate fabrication of nutrition information.Our system treats nutrient estimation and entity linking not as a generative problem (predicting numbers from scratch), but as a retrieval problem.We operate on the hypothesis that for structured data like food composition, finding a "real' recipe that is 95% similar is more likely to approximate the correct values than letting the language model fabricate values from sparse context.Our system performed well on food safety labeling from recipe ingredients alone, and it did not benefit from the additional information of recipe titles.In the NER and NEL tasks, our system handled the recipe-focused FCD corpus well, but suffered from poor recall on scientific abstracts and the artificial dataset.These results show the importance of basing information retrieval and question answering in data that is well-matched to the target data.
Authors: Ravi Ranjan, Roshan Santhosh, Lucien Carroll
Institutions: GLA University, Adobe Systems (United States), Cohere (Canada)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W7166587153