arXiv cs.AI· Saransh Kumar Gupta, Armaan Shah, Lipika Dey, Partha Pratim Das, Ramesh Jain·· 3 小时前AI 评分21
FKG.in 验证：LLM 增强的印度食物知识图谱可靠性评估
Validating FKG.in: Soundness Assessment in LLM-Augmented Indian Food Knowledge
AI 导读
印度食物知识图谱 FKG.in 的团队提出一套半自动化健全性评估流程，用于校验从非正式烹饪来源提取并由 LLM 增强的结构化食谱数据。该流程通过形式文法、词表检查、统计启发式、Set Transformer 一致性建模和检索式验证等多阶段方法，识别结构不一致、语义逻辑不连贯及偏离原文等失败模式。方法以印度食谱评测，但可推广至多语言、多文化的烹饪领域。
正文 · 原文
Abstract:The online culinary ecosystem is increasingly populated by recipe content generated, modified, or summarized by LLMs. Although often convincing, 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 from informal culinary sources and augmented by LLMs. 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 approach to improving the reliability of machine-readable food knowledge infrastructures in the era of LLM-generated content.
| Comments: | 20 pages, 2 figures, 5 tables, 29 references |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.29249 [cs.AI] |
| (or arXiv:2608.29249v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.29249 arXiv-issued DOI via DataCite |
Submission history
From: Saransh Gupta Mr. [view email]
[v1]
Sat, 29 Aug 2026 13:10:06 UTC (775 KB)
[v2]
Tue, 1 Sep 2026 03:34:21 UTC (775 KB)
[v3]
Tue, 27 Oct 2026 04:22:54 UTC (758 KB)
来源：arXiv cs.AI · arxiv.org