Task Vectors are Cross-Modal

ICLR 2025 Conference Submission12334 Authors

Keywords: in-context learning, interpretability, multimodal models, vision and language models, task vectors, function vectors

Abstract: We investigate the internal representations of autoregressive 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.

Primary Area: interpretability and explainable AI

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Submission Number: 12334