A benchmark for assessing large language models on molecular-to-food and food-to-molecular prediction tasks
Large Language Models (LLMs) have demonstrated remarkable proficiency in general-purpose tasks, yet their capacity for fine-grained reasoning in knowledge-intensive domains (KIDs) remains largely unexplored. This study addresses this gap by investigating LLM performance in the specialized field of...
Annons
Annons
Large Language Models (LLMs) have demonstrated remarkable proficiency in general-purpose tasks, yet their capacity for fine-grained reasoning in knowledge-intensive domains (KIDs) remains largely unexplored. This study addresses this gap by investigating LLM performance in the specialized field of food chemistry. We introduce a novel benchmark comprising two core tasks: Molecular-to-Food Prediction (MFP) and Food-to-Molecular Prediction (FMP). To support this benchmark, we curated and standardized the FlavorDB dataset, creating a robust testbed for flavor-molecular association. We evaluated two open-source models (Kimi-K2, DeepSeek-V3.2) and three closed-source models (Gemini-3-Pro, GPT-5.1, and Seed-1.8) under zero-shot and one-shot in-context learning settings. Our systematic analysis yields six key findings that characterize the capabilities and limitations of current LLMs in this domain. For instance, in the FMP task, Gemini-3-Pro achieved the highest zero-shot F1 score of 0.556, while Kimi-K2 led the one-shot setting with an F1 score of 0.522. In-context learning consistently improved performance across models, most notably boosting Kimi-K2's F1 score from 0.451 to 0.496 on complex multi-food tasks. Critically, we identify four recurring categories of domain-specific reasoning errors, which illuminate the fundamental challenges general-purpose models face when applied to fine-grained scientific inference in food chemistry. This work not only establishes a framework for rigorously evaluating LLM potential in knowledge-intensive domains but also provides a critical foundation for advancing practical applications, including flavor optimization and the development of novel food products.