MM-SAP：评估多模态大语言模型感知自我意识的综合基准
MM-SAP: A Comprehensive Benchmark for Assessing Self-Awareness of Multimodal Large Language Models in Perception
AI总结：
本文提出MM-SAP基准，通过引入感知知识象限框架来定义并评估多模态大语言模型在感知中的自我意识，实验表明当前模型自我意识能力有限，为未来开发可信MLLMs指明方向。
摘要：
多模态大语言模型（MLLMs）的最新进展展示了其在视觉感知与理解方面的卓越能力。然而，这些模型也存在幻觉问题，这限制了其作为AI系统的可靠性。我们认为，这些幻觉部分源于模型难以理解自身能从图像中感知到什么以及不能感知到什么，我们将这种能力称为感知中的自我意识。尽管这很重要，但MLLMs的这一方面在先前的研究中被忽视了。在本文中，我们旨在定义并评估MLLMs在感知中的自我意识。为此，我们首先引入了感知知识象限，以帮助定义MLLMs对图像知道什么和不知道什么。利用这一框架，我们提出了一个新颖的基准——MLLMs感知自我意识（MM-SAP），专门用于评估这一能力。我们将MM-SAP应用于多种流行的MLLMs，对其自我意识进行了全面分析，并提供了详细的见解。实验结果表明，当前的MLLMs具备有限的自我意识能力，这指出了未来开发可信MLLMs的一个关键领域。代码和数据可在 https://github.com/YHWmz/MM-SAP 获取。
英文摘要：
Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in visual perception and understanding. However, these models also suffer from hallucinations, which limit their reliability as AI systems. We believe that these hallucinations are partially due to the models' struggle with understanding what they can and cannot perceive from images, a capability we refer to as self-awareness in perception. Despite its importance, this aspect of MLLMs has been overlooked in prior studies. In this paper, we aim to define and evaluate the self-awareness of MLLMs in perception. To do this, we first introduce the knowledge quadrant in perception, which helps define what MLLMs know and do not know about images. Using this framework, we propose a novel benchmark, the Self-Awareness in Perception for MLLMs (MM-SAP), specifically designed to assess this capability. We apply MM-SAP to a variety of popular MLLMs, offering a comprehensive analysis of their self-awareness and providing detailed insights. The experiment results reveal that current MLLMs possess limited self-awareness capabilities, pointing to a crucial area for future advancement in the development of trustworthy MLLMs. Code and data are available at https://github.com/YHWmz/MM-SAP.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
