用嵌入向量揭示食材风味的多维结构，让烹饪直觉可被系统解析。
Epicure: Multidimensional Flavor Structure in Food Ingredient Embeddings
- 基于食谱共现与食品化学训练的300维嵌入，编码烹饪直觉
- 通过大模型清理后，1032个标准食材项显现出至少15维可分类特征
- 适合对风味科学、食物文化或生成式烹饪感兴趣的研究者
厨师对风味、口感和文化身份的直觉是难以言说却至关重要的隐性知识。我们发现，这一知识已编码在基于食谱共现与食品化学训练的300维风味图嵌入（FlavorGraph）中，并可被系统性挖掘。通过大语言模型增强的整理流程，6,653个原始食材条目被归一化为1,032个标准条目，显著增强了可恢复的结构。我们识别出至少十五个独立可分类的维度，涵盖味道、质地、地理、加工方式及文化属性。
原文摘要 · Abstract (English)
A chef's intuition about flavor, texture, and cultural identity represents tacit knowledge that is difficult to articulate yet central to culinary practice. We show that this knowledge is already encoded in FlavorGraph's 300-dimensional ingredient embeddings, trained on recipe cooccurrence and food chemistry, and that it can be systematically recovered. An LLM-augmented curation pipeline consolidates 6,653 raw FlavorGraph ingredients into 1,032 canonical entries, substantially strengthening the recoverable structure. We identify at least fifteen independently classifiable dimensions spanning taste, texture, geography, food processing, and culture.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。