Problem
Research questions and friction points this paper is trying to address.
Large Language Models
recipe content
knowledge graph
culinary data validation
soundness assessment
Validating FKG.in: Soundness Assessment in LLM-Augmented Indian Food Knowledge
๐ค AI Summary
ๆฌๆๆๅบไธ็งๅ่ชๅจๅ่ฏไผฐๆต็จ๏ผ้่ฟๅค็งๆๆฏ็ปๅ็ๆนๆณ่งฃๅณLLM็ๆ็ๅฐๅบฆ้ฃ่ฐฑๆฐๆฎไธญ็้่ฏฏๅไธไธ่ดๆง้ฎ้ขใ
๐ Abstract
The online culinary ecosystem is increasingly populated by recipe content generated, modified, or summarized by Large Language Models (LLMs). While often plausible, such outputs may contain hallucinated ingredients, misrepresented quantities, or culturally implausible combinations, limiting their suitability for downstream applications and knowledge graph construction. In this paper, we present a semi-automated soundness assessment workflow for validating structured recipe data extracted and augmented by LLMs from informal culinary sources. Developed as part of FKG.in, a knowledge graph of Indian food, the pipeline identifies and addresses common failure modes, including structural inconsistencies, semantic and logical incoherence, and deviations from the source text, through a multi-stage process combining formal grammars, vocabulary-based checks, statistical heuristics, Set Transformer-based coherence modeling, and retrieval-based verification. Although evaluated on Indian recipes, the proposed methods are applicable to broader multilingual and multicultural culinary domains. We provide a practical, auditable, and application-agnostic framework for validating LLM-augmented recipe data, thereby strengthening the foundations of machine-readable food knowledge infrastructures in the era of LLM-generated content.
Innovation
Methods, ideas, or system contributions that make the work stand out.
soundness assessment
large language models (LLMs)
structured recipe data
set transformer
retrieval-based verification
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