Large Vision-Language Models (LVLMs) excel in cross-model tasks but experience performance declines in long-context reasoning due to overreliance on textual information and reduced visual dependency. In this study, we empirically analyze LVLMs in long-context reasoning, revealing that increased context length leads to a higher dependence on language at the expense of visual dependency. To address this issue, we propose a novel training-free context pruning method that selectively removes less critical textual information. Our approach enhances visual dependency and reduces textual noise, thereby improving LVLM performance in long-context reasoning. We validate our method by constructing a long-context dataset, demonstrating its effectiveness across various LVLMs. Moreover, further analysis confirms the robustness of different token pruning strategies and preliminary explores scaling laws between pruning rates and context length.
@article{arxiv.2410.19732,
title = {Rethinking Visual Dependency in Long-Context Reasoning for Large Vision-Language Models},
author = {Yucheng Zhou and Zhi Rao and Jun Wan and Jianbing Shen},
journal= {arXiv preprint arXiv:2410.19732},
year = {2024}
}