Can Transformers Smell Like Humans?

Farzaneh Taleb, Miguel Vasco, Antonio H. Ribeiro, Mårten Björkman, Danica Kragic

Keywords: Representational Alignment, Olfactory, Transformers, Representation Learning, Neuroscience

TL;DR: In this work, we evaluate representational alignment between human olfactory perception and representations extracted from pre-trained transformers.

Abstract: The human brain encodes stimuli from the environment into representations that form a sensory perception of the world. Despite recent advances in understanding visual and auditory perception, olfactory perception remains an under-explored topic in the machine learning community due to the lack of large-scale datasets annotated with labels of human olfactory perception. In this work, we ask the question of whether pre-trained transformer models of chemical structures encode representations that are aligned with human olfactory perception, i.e., can transformers smell like humans? We demonstrate that representations encoded from transformers pre-trained on general chemical structures are highly aligned with human olfactory perception. We use multiple datasets and different types of perceptual representations to show that the representations encoded by transformer models are able to predict: (i) labels associated with odorants‌‌ provided by experts; (ii) continuous ratings provided by human participants with respect to pre-defined descriptors; and (iii) similarity ratings between odorants provided by human participants. Finally, we evaluate the extent to which this alignment is associated with physicochemical features of odorants known to be relevant for olfactory decoding.

Primary Area: Neuroscience and cognitive science (neural coding, brain-computer interfaces)

Submission Number: 16622