title: LVLM-Intrepret: An Interpretability Tool for Large Vision-Language
    Models
summary: 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.
paper_authors._id: 660f676594fa5c852e2cda4d
paper_authors.name: Gabriela Ben Melech Stan
paper_authors._id: 660f676594fa5c852e2cda4e
paper_authors.name: Raanan Yehezkel Rohekar
paper_authors._id: 660f676594fa5c852e2cda4f
paper_authors.name: Yaniv Gurwicz
paper_authors._id: 660f676594fa5c852e2cda50
paper_authors.name: Matthew Lyle Olson
paper_authors._id: 660f676594fa5c852e2cda51
paper_authors.name: Anahita Bhiwandiwalla
paper_authors._id: 660f676594fa5c852e2cda52
paper_authors.name: Estelle Aflalo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-18T09:55:06.078Z
paper_authors._id: 64575266900b28f7841090e5
paper_authors.avatarUrl: /avatars/13dc0a370bb24271653c352897a2673e.svg
paper_authors.fullname: Estelle Aflalo
paper_authors.type: user
paper_authors.user: estellea
paper_authors._id: 660f676594fa5c852e2cda53
paper_authors.name: Chenfei Wu
paper_authors._id: 660f676594fa5c852e2cda54
paper_authors.name: Nan Duan
paper_authors._id: 660f676594fa5c852e2cda55
paper_authors.name: Shao-Yen Tseng
paper_authors._id: 660f676594fa5c852e2cda56
paper_authors.name: Vasudev Lal
paper_title: LVLM-Intrepret: An Interpretability Tool for Large Vision-Language
    Models
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: multi-modal large language models explainability tools interpretability vision-language models image patches grounding system limitations LLaVA