探究多模态大语言模型（MLLMs）在低层感知中的幻觉问题
Explore the Hallucination on Low-level Perception for MLLMs
AI总结：
针对多模态大语言模型（MLLMs）在低层视觉感知任务中的幻觉问题，提出QL-Bench基准与LLSAVisionQA数据集，评估15款模型后发现其低层视觉能力与自我感知能力不匹配，为相关研究提供支撑。
摘要：
多模态大语言模型（Multi-modality Large Language Models, MLLMs）的快速发展已对工业生产和日常生活的诸多方面产生显著影响，展现出令人瞩目的视觉感知与理解能力。然而，这类模型也存在幻觉现象，这一问题限制了其作为AI系统的可靠性，在涉及低层视觉感知与理解的任务中尤为突出。我们认为，幻觉的根源在于这些模型缺乏明确的自我感知能力，而这一缺陷直接影响其整体性能。\n本文旨在定义并评估MLLMs在低层视觉感知与理解任务中的自我感知能力。为此，我们提出了QL-Bench——一种模拟人类对低层视觉反应的基准设置，通过与清晰度、光照等低层属性相关的视觉问答，探究模型在低层视觉感知中的自我感知水平。具体而言，我们构建了LLSAVisionQA数据集，包含2990张单幅图像和1999组图像对，每组样本都配有一道关于其低层特征的开放式问题。\n通过对15款MLLMs的评估，我们发现：尽管部分模型具备较强的低层视觉能力，但其自我感知能力仍相对薄弱。值得注意的是，对于同一模型，简单问题的回答准确率通常高于复杂问题；但在处理更具挑战性的问题时，模型的自我感知水平会有所提升。我们希望该基准能推动后续研究，尤其聚焦于提升MLLMs在低层视觉感知与理解任务中的自我感知能力。
英文摘要：
The rapid development of Multi-modality Large Language Models (MLLMs) has significantly influenced various aspects of industry and daily life, showcasing impressive capabilities in visual perception and understanding. However, these models also exhibit hallucinations, which limit their reliability as AI systems, especially in tasks involving low-level visual perception and understanding. We believe that hallucinations stem from a lack of explicit self-awareness in these models, which directly impacts their overall performance. In this paper, we aim to define and evaluate the self-awareness of MLLMs in low-level visual perception and understanding tasks. To this end, we present QL-Bench, a benchmark settings to simulate human responses to low-level vision, investigating self-awareness in low-level visual perception through visual question answering related to low-level attributes such as clarity and lighting. Specifically, we construct the LLSAVisionQA dataset, comprising 2,990 single images and 1,999 image pairs, each accompanied by an open-ended question about its low-level features. Through the evaluation of 15 MLLMs, we demonstrate that while some models exhibit robust low-level visual capabilities, their self-awareness remains relatively underdeveloped. Notably, for the same model, simpler questions are often answered more accurately than complex ones. However, self-awareness appears to improve when addressing more challenging questions. We hope that our benchmark will motivate further research, particularly focused on enhancing the self-awareness of MLLMs in tasks involving low-level visual perception and understanding.
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