  Language models demonstrate remarkable capacity to generalize representations
learned in one modality to downstream tasks in other modalities. Can we trace
this ability to individual neurons? We study the case where a frozen text
transformer is augmented with vision using a self-supervised visual encoder and
a single linear projection learned on an image-to-text task. Outputs of the
projection layer are not immediately decodable into language describing image
content; instead, we find that translation between modalities occurs deeper
within the transformer. We introduce a procedure for identifying "multimodal
neurons" that convert visual representations into corresponding text, and
decoding the concepts they inject into the model's residual stream. In a series
of experiments, we show that multimodal neurons operate on specific visual
concepts across inputs, and have a systematic causal effect on image
captioning.
