An LLM-driven Chinese Corpus of Human Olfactory Descriptions and Entity Annotations

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

Language plays a pivotal role in artificial olfactory perception, serving as a crucial bridge that translates chemical stimuli into human experience. The mapping from olfactory stimuli to linguistic descriptions constitutes the foundation for modeling artificial olfaction with human-like descriptive capabilities. However, this field is currently constrained by a lack of publicly available, culturally diverse olfactory corpora, with existing datasets predominantly reflecting Western odor profiles. To address this gap, we constructed a large-scale Chinese olfactory corpus by employing a dual-iterative strategy assisted by a large language model (LLM). Beginning with an initial set of standardized descriptors and a defined annotation template, our strategy iteratively alternated between lexicon expansion and corpus refinement throughout the process. The resulting large-scale corpus of domain-relevant sentences enables the training of computational language models. A downstream olfactory semantic clustering task was performed. The result demonstrates that language model fine-tuning on the proposed corpus improves embeddings for capturing Chinese olfactory experience. Consequently, our work provides an essential foundational resource for advancing culturally inclusive models of olfactory perception.

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