表面化变异以校准MLLM生成图像描述的可信度感知
Surfacing Variations to Calibrate Perceived Reliability of MLLM-generated Image Descriptions
AI总结：
通过系统性地展示多个MLLM响应间的变异，帮助盲人或低视力用户无需视觉检查即可检测不可靠信息，实验表明该方法将识别不可靠声明的能力提升4.9倍。
摘要：
多模态大语言模型（MLLM）为盲人或低视力（BLV）人群在日常生活中获取视觉信息提供了新机会。然而，这些模型经常产生在没有视觉的情况下难以检测的错误，从药物识别到服装选择等场景中带来安全和社会风险。虽然BLV MLLM用户使用创造性的变通方法，如跨工具交叉检查和咨询视力正常的人，但这些方法通常耗时且不切实际。我们探索如何系统性地表面化多个MLLM响应之间的变异，以支持BLV用户无需视觉检查图像即可检测不可靠信息。我们贡献了一个用于引发和呈现MLLM描述变异的设计空间，一个实现三种变异呈现风格的原型系统，以及一项涉及15名BLV参与者的用户研究结果。我们的结果表明，呈现变异显著提高了用户识别不可靠声明的能力（与单一描述相比，使用我们的方法提高了4.9倍），并显著降低了对MLLM响应的可信度感知。15名参与者中有14人更喜欢看到MLLM响应的变异而不是单一描述，所有人都表示有兴趣使用我们的系统完成从理解龙卷风路径到在社交媒体上发布图像等任务。
英文摘要：
Multimodal large language models (MLLMs) provide new opportunities for blind and low vision (BLV) people to access visual information in their daily lives. However, these models often produce errors that are difficult to detect without sight, posing safety and social risks in scenarios from medication identification to outfit selection. While BLV MLLM users use creative workarounds such as cross-checking between tools and consulting sighted individuals, these approaches are often time-consuming and impractical. We explore how systematically surfacing variations across multiple MLLM responses can support BLV users to detect unreliable information without visually inspecting the image. We contribute a design space for eliciting and presenting variations in MLLM descriptions, a prototype system implementing three variation presentation styles, and findings from a user study with 15 BLV participants. Our results demonstrate that presenting variations significantly increases users' ability to identify unreliable claims (by 4.9x using our approach compared to single descriptions) and significantly decreases perceived reliability of MLLM responses. 14 of 15 participants preferred seeing variations of MLLM responses over a single description, and all expressed interest in using our system for tasks from understanding a tornado's path to posting an image on social media.
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。
