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

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 foundational basis 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 with the assistance of 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, comprising highly domain-relevant sentences, enables the training of computational language model. The model achieves a more nuanced and perceptually grounded semantic mapping of the Chinese olfactory experience. Consequently, our work provides an essential foundational resource for advancing culturally inclusive models of olfactory perception.