Title: Where do Large Vision-Language Models Look at when Answering Questions?
Primary area: interpretability and explainable AI
Keywords: VLM, interpretation

Abstract: Large Vision-Language Models (LVLMs) have shown promising performance in vision-language understanding and reasoning tasks. However, whether they truly understand the input image remain underexplored. A fundamental question arises: to what extent do LVLMs rely on visual input, and which image regions contribute to their responses? It is non-trivial to interpret the free-form generation of LVLMs due to their complicated visual architecture (e.g., multiple encoders and multi-resolution) and variable-length outputs. In this paper, we extend existing heatmap visualization methods for classification tasks to support LVLMs for open-ended visual question answering. We propose a method to select visually relevant tokens that reflect the relevance between generated responses and the input image.
Furthermore, we conduct a comprehensive analysis of state-of-the-art LVLMs on benchmarks designed to require visual information to answer. Our findings offer several insights into LVLM behavior, including the relationship between focus region and answer correctness, differences in visual attention across architectures, and the impact of LLM scale on visual understanding. The code and data will be
released