LVLM-Intrepret: An Interpretability Tool for Large Vision-Language
  Models
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.
660f676594fa5c852e2cda4d
Gabriela Ben Melech Stan
660f676594fa5c852e2cda4e
Raanan Yehezkel Rohekar
660f676594fa5c852e2cda4f
Yaniv Gurwicz
660f676594fa5c852e2cda50
Matthew Lyle Olson
660f676594fa5c852e2cda51
Anahita Bhiwandiwalla
660f676594fa5c852e2cda52
Estelle Aflalo
claimed_verified
2024-12-18T09:55:06.078Z
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/avatars/13dc0a370bb24271653c352897a2673e.svg
Estelle Aflalo
user
estellea
660f676594fa5c852e2cda53
Chenfei Wu
660f676594fa5c852e2cda54
Nan Duan
660f676594fa5c852e2cda55
Shao-Yen Tseng
660f676594fa5c852e2cda56
Vasudev Lal
LVLM-Intrepret: An Interpretability Tool for Large Vision-Language
  Models
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.
An interactive application enhances interpretability of vision-language models by analyzing image patches and assessing language model's grounding in images, with a case study on LLaVA.
multi-modal large language models explainability tools interpretability vision-language models image patches grounding system limitations LLaVA