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挑战多模式任务中的分心:大型视觉语言模型的新基准
IEEE transactions on pattern analysis and machine intelligence
|January 19, 2026
概括
大视觉语言模型 (LVLMs) 是不可靠的,因为"多模式分心". 本研究引入了一个具有基准,新指标 (PC/NC) 和路由机制 (RGMR) 的框架,以提高它们的推理一致性和稳定性.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 大视觉语言模型 (LVLMs) 容易受到"多模式分心".
- 不相关的视觉或文字输入降低了LVLM推理的一致性和可靠性.
- 现有的评估方法无法充分捕捉这些漏洞.
研究的目的:
- 引入一个全面的框架来诊断,评估和减轻LVLMs的多式联运分心.
- 开发新的方法来测量超出标准准确度的推理稳定性.
- 提出一种机制,在推理过程中主动过分心.
主要方法:
- 开发大规模的IR-VQA基准来识别LVLM漏洞.
- 引入正一致性 (PC) 和负一致性 (NC) 度量来评估推理稳定性.
- 实现关联式多式联运路由 (RGMR) 机制,用于动态分心过.
主要成果:
- 最先进的LVLM在IR-VQA基准指标上显示出显著的一致性下降.
- 对IR-VQA的微调和使用RGMR大大提高了模型的稳定性.
- 在标准提示方法失败的情况下,RGMR在提高可靠性方面被证明是有效的.
结论:
- 多式联运的分心能力是当前LVLMs的一个关键挑战.
- 拟议的框架,指标和RGMR机制为实现更可靠的多式联运系统提供了可行的途径.
- 对分散注意力的模型行为的进一步分析为未来的研究提供了洞察力.
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