超越提示：通过随机引导实现认知障碍标准化患者细粒度模拟
Beyond Prompt: Fine-grained Simulation of Cognitively Impaired Standardized Patients via Stochastic Steering
AI总结：
本文提出StsPatient方法，通过提取对比指令与响应的引导向量，实现对认知障碍患者细粒度模拟，提升临床真实性与严重程度可控性。
摘要：
模拟具有认知障碍的标准化患者为临床培训提供了可扩展且伦理的解决方案。然而，现有方法依赖离散提示工程，无法捕捉不同领域和严重程度的缺陷异质性。为解决这一限制，我们提出了StsPatient，用于细粒度模拟认知障碍患者。我们创新性地通过提取对比指令与响应的引导向量来捕捉领域特定特征。此外，我们引入了随机令牌调节（STM）机制来调节干预概率。STM能够在不牺牲稳定性的情况下实现对障碍严重程度的精确控制。全面实验表明，StsPatient在临床真实性和严重程度可控性方面显著优于基线方法。
英文摘要：
Simulating Standardized Patients with cognitive impairment offers a scalable and ethical solution for clinical training. However, existing methods rely on discrete prompt engineering and fail to capture the heterogeneity of deficits across varying domains and severity levels. To address this limitation, we propose StsPatient for the fine-grained simulation of cognitively impaired patients. We innovatively capture domain-specific features by extracting steering vectors from contrastive pairs of instructions and responses. Furthermore, we introduce a Stochastic Token Modulation (STM) mechanism to regulate the intervention probability. STM enables precise control over impairment severity while mitigating the instability of conventional vector methods. Comprehensive experiments demonstrate that StsPatient significantly outperforms baselines in both clinical authenticity and severity controllability.
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。
