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Embeddings from contemporary natural language processing (NLP) models arecommonly used as numerical representations for words or sentences. However,odor descriptor words, like "leather" or "fruity", vary significantly betweentheir commonplace usage and their olfactory usage, as a result traditionalmethods for generating these embeddings do not suffice. In this paper, wepresent two methods to generate embeddings for odor words that are more closelyaligned with their olfactory meanings when compared to off-the-shelfembeddings. These generated embeddings outperform the previous state-of-the-artand contemporary fine-tuning/prompting methods on a pre-existing zero-shotodor-specific NLP benchmark.

Odor Descriptor Understanding through Prompting