LVLM-Intrepret: An Interpretability Tool for Large Vision-Language Models

Gabriela Ben-Melech Stan, Estelle Aflalo, Raanan Yehezkel Rohekar, Anahita Bhiwandiwalla, Shao-Yen Tseng, Matthew Lyle Olson, Yaniv Gurwicz, Chenfei Wu, Nan Duan, Vasudev Lal

Abstract:

In the rapidly evolving landscape of artificial intelligence, multi-modal large language models are emerging as
a significant area of interest. These models, which combine
various forms of data input, are becoming increasingly popular. However, understanding their internal mechanisms remains a complex task. Numerous advancements have been
made in the field of explainability tools and mechanisms,
yet there is still much to explore. In this work, we present a
novel interactive application aimed towards understanding
the internal mechanisms of large vision-language models.
Our interface is designed to enhance the interpretability of
the image patches, which are instrumental in generating an
answer, and assess the efficacy of the language model in
grounding its output in the image. With our application, a
user can systematically investigate the model and uncover
system limitations, paving the way for enhancements in system capabilities. Finally, we present a case study of how our
application can aid in understanding failure mechanisms in
a popular large multi-modal model: LLaVA.