用随机调控模拟认知障碍患者，让临床训练更真实可控。
Beyond Prompt: Fine-grained Simulation of Cognitively Impaired Standardized Patients via Stochastic Steering
- 通过对比指令提取导向向量，实现不同认知领域缺陷的精准建模。
- 引入随机分词调节机制，显著提升严重程度控制精度与稳定性。
- 适合医学教育、智能诊疗系统训练等需要高仿真患者的应用场景。
模拟认知障碍标准化患者为临床培训提供了可扩展且合乎伦理的解决方案。然而，现有方法依赖离散提示工程，难以捕捉不同认知领域和严重程度下的缺陷异质性。为此，我们提出StsPatient，用于细粒度模拟认知障碍患者。创新性地通过对比指令与响应对提取领域特异性引导向量，实现对认知缺陷的精准刻画；同时引入随机分词调节（STM）机制，调控干预概率，可在保持稳定性的同时精确控制缺陷严重程度。大量实验表明，StsPatient在临床真实性与严重程度可控性方面均显著优于基线模型。
原文摘要 · Abstract (English)
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.
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