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English(EN) Small Vision-Language Models Know When They Are Wrong But Cannot Say So: A Two-Model Study of Stated versus Internal Confidence Under Realistic Image Degradation

小型视觉语言模型在图像退化下表现出置信度差距

作者 PulseAugur 编辑部 · [1 个来源] ·

一项近期研究考察了Qwen2-VL-2B-Instruct和SmolVLM-Instruct这两个小型视觉语言模型在真实图像退化下的置信度信号。研究发现，在自然语言中声明的置信度与内部标记概率之间存在显著差异。虽然内部概率被证明是更可靠的错误指标，但在面对严重曝光不足时，两个模型都难以准确评估其置信度，导致准确性大幅下降，而置信度信号变化甚微。 AI

影响 凸显了当前小型视觉语言模型在可靠不确定性估计方面的局限性，这对于安全部署至关重要。

排序理由 学术论文，详细介绍了模型能力的研究结果。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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小型视觉语言模型在图像退化下表现出置信度差距

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学术论文，详细介绍了模型能力的研究结果。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · M M Asif Ferdous ·

    小型视觉语言模型知道自己何时出错但无法表达：一项关于真实图像退化下声明置信度与内部置信度的双模型研究

    arXiv:2607.22034v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly deployed on consumer hardware where input images are degraded by compression, camera shake, and poor lighting. In such settings, a reliable uncertainty signal matters more than raw ac…

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