[2603.03176] FEAST: Retrieval-Augmented Multi-Hierarchical Food Classification for the FoodEx2 System

Computer Science > Artificial Intelligence arXiv:2603.03176 (cs) [Submitted on 3 Mar 2026] Title:FEAST: Retrieval-Augmented Multi-Hierarchical Food Classification for the FoodEx2 System Authors:Lorenzo Molfetta, Alessio Cocchieri, Stefano Fantazzini, Giacomo Frisoni, Luca Ragazzi, Gianluca Moro View a PDF of the paper titled FEAST: Retrieval-Augmented Multi-Hierarchical Food Classification for the FoodEx2 System, by Lorenzo Molfetta and 5 other authors View PDF HTML (experimental) Abstract:Hierarchical text classification (HTC) and extreme multi-label classification (XML) tasks face compounded challenges from complex label interdependencies, data sparsity, and extreme output dimensions. These challenges are exemplified in the European Food Safety Authority's FoodEx2 system-a standardized food classification framework essential for food consumption monitoring and contaminant exposure assessment across Europe. FoodEx2 coding transforms natural language food descriptions into a set of codes from multiple standardized hierarchies, but faces implementation barriers due to its complex structure. Given a food description (e.g., "organic yogurt''), the system identifies its base term ("yogurt''), all the applicable facet categories (e.g., "production method''), and then, every relevant facet descriptors to each category (e.g., "organic production''). While existing models perform adequately on well-balanced and semantically dense hierarchies, no work has been applied on the practi...