提出医疗视觉语言模型抗逢迎行为的评测与缓解方法。
Benchmarking and Mitigating Sycophancy in Medical Vision Language Models
- 构建分层医疗视觉问答评测框架，多模板测试模型表现。
- 发现模型对权威暗示敏感，错误率与模型大小正相关。
- 提出VIPER策略过滤社交线索，提升推理可靠性。
视觉语言模型（VLMs）有潜力改变医疗工作流程，但其部署受限于‘逢迎’现象。尽管这对患者安全构成严重威胁，系统性评测仍缺失。本文引入一个医疗基准，通过多种模板在分层医疗视觉问答任务中测试VLMs。结果发现，当前模型高度依赖视觉线索，错误率与模型规模或整体准确率呈正相关；我们还发现感知权威性和用户模仿是强大触发因素，表明存在独立于视觉数据的偏见机制。为此，我们提出视觉信息净化策略（VIPER），主动过滤非证据性社会线索，强化基于证据的推理。VIPER在保持可解释性的同时显著降低逢迎行为，并持续优于基线方法，为VLMs的稳健与安全集成奠定了基础。
原文摘要 · Abstract (English)
Visual language models (VLMs) have the potential to transform medical workflows. However, the deployment is limited by sycophancy. Despite this serious threat to patient safety, a systematic benchmark remains lacking. This paper addresses this gap by introducing a Medical benchmark that applies multiple templates to VLMs in a hierarchical medical visual question answering task. We find that current VLMs are highly susceptible to visual cues, with failure rates showing a correlation to model size or overall accuracy. we discover that perceived authority and user mimicry are powerful triggers, suggesting a bias mechanism independent of visual data. To overcome this, we propose a Visual Information Purification for Evidence based Responses (VIPER) strategy that proactively filters out non-evidence-based social cues, thereby reinforcing evidence based reasoning. VIPER reduces sycophancy while maintaining interpretability and consistently outperforms baseline methods, laying the necessary foundation for the robust and secure integration of VLMs.
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