Odor Descriptor Understanding through Prompting
This addresses the challenge of accurately representing odor descriptors in NLP for applications in domains like perfumery or food science, but it is incremental as it builds on existing embedding and prompting methods.
The paper tackled the problem of generating embeddings for odor descriptor words that better capture their olfactory meanings, achieving state-of-the-art performance on a zero-shot odor-specific NLP benchmark.
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.