  Mechanistic interpretability seeks to understand the neural mechanisms that
enable specific behaviors in Large Language Models (LLMs) by leveraging
causality-based methods. While these approaches have identified neural circuits
that copy spans of text, capture factual knowledge, and more, they remain
unusable for multimodal models since adapting these tools to the
vision-language domain requires considerable architectural changes. In this
work, we adapt a unimodal causal tracing tool to BLIP to enable the study of
the neural mechanisms underlying image-conditioned text generation. We
demonstrate our approach on a visual question answering dataset, highlighting
the causal relevance of later layer representations for all tokens.
Furthermore, we release our BLIP causal tracing tool as open source to enable
further experimentation in vision-language mechanistic interpretability by the
community. Our code is available at
https://github.com/vedantpalit/Towards-Vision-Language-Mechanistic-Interpretability.
