The application of large language models (LLMs) to OdorSpace analysis attracts growing interest. Recent studies have explored the comparison of sensory evaluation spaces derived from LLMs with odor character profiles in the Dravnieks' dataset. In this study, we calculated pairwise distances of odor descriptors using three distance measures and statistically compared these LLM-derived similarities with distances derived from the original data. Next, we extended this approach to odor names (ingredients). Statistical comparison revealed that LLMs can infer odor similarity to some degree, suggesting the potential of odor maps generated from these similarity data. Applying this approach, we generated an odor map of essential oils. It demonstrates that essential oils within the same group are closely located in the odor map, suggesting that the proximity in the odor map corresponds to human evaluation.

翻译：大语言模型在OdorSpace分析中的应用日益引起关注。近期研究探索了从大语言模型衍生出的感官评价空间与Dravnieks数据集中气味特征画像的对比。本研究采用三种距离度量计算了气味描述符之间的成对距离，并统计比较了这些基于大语言模型推导的相似性与原始数据导出的距离。接着，我们将该方法扩展至气味名称（成分）。统计比较表明，大语言模型能在一定程度上推断气味相似性，从而揭示了基于这些相似性数据生成嗅觉地图的潜力。应用该方法，我们生成了一幅精油的嗅觉地图。该地图显示，属于同一类别的精油在嗅觉地图中位置相近，表明该地图中的邻近关系与人类评估结果相对应。