Task Vectors are Cross-Modal
Authors: name: Grace Luo, name: Trevor Darrell, name: Amir Bar

We investigate the internal representations of vision-and-language models
(VLMs) and how they encode task representations. We consider tasks specified
through examples or instructions, using either text or image inputs.
Surprisingly, we find that conceptually similar tasks are mapped to similar
task vector representations, regardless of how they are specified. Our findings
suggest that to output answers, tokens in VLMs undergo three distinct phases:
input, task, and answer, a process which is consistent across different
modalities and specifications. The task vectors we identify in VLMs are general
enough to be derived in one modality (e.g., text) and transferred to another
(e.g., image). Additionally, we find that ensembling exemplar and instruction
based task vectors produce better task representations. Taken together, these
insights shed light on the underlying mechanisms of VLMs, particularly their
ability to represent tasks in a shared manner across different modalities and
task specifications. Project page:
https://task-vectors-are-cross-modal.github.io.