POMMix, a graph-based model with attention and cosine similarity heads, extends the Principal Odor Map to predict human perceptual similarity of odor mixtures, reporting a test correlation of 0.78 on a compiled dataset of 865 mixture pairs.
Odor Descriptor Understanding through Prompting
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abstract
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.
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From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases
POMMix, a graph-based model with attention and cosine similarity heads, extends the Principal Odor Map to predict human perceptual similarity of odor mixtures, reporting a test correlation of 0.78 on a compiled dataset of 865 mixture pairs.