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