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
this https URL

通过引入一种单模态因果追踪工具，我们适应了 BLIP 以研究图像条件下文本生成的神经机制，并在视觉问答数据集上展示了我们的方法，强调了较晚层表示对所有标记的因果相关性。此外，我们将我们的 BLIP 因果追踪工具开源，以便社区进一步探索视觉语言机制可解释性。