发表机构
University of Alicante; Technische Universität Darmstadt; Hessian Center for AI (hessian.AI)（阿利坎特大学; 达姆施塔特工业大学; 黑森人工智能中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出ODRA框架，结合结构化思维链与动态患者阻抗建模合成CBT会话，解决现有方法的顺从性问题，其生成的会话及微调数据集可提升下游治疗性能。
AI 中文摘要
认知行为治疗（CBT）会话的合成面临两个相互冲突的需求：既要遵循严格的治疗结构，又要建模真实患者的阻抗、不可预测的行为。现有基于脚本的方法无法捕捉动态治疗互动，多智能体方法则难以遵循CBT的顺序结构；两者均存在顺从性问题，生成过于顺从的患者，无法代表真实临床场景。本研究提出ODRA，一种基于基础CBT指南（Beck，2020）的思维链（CoT）策略合成治疗对话的新框架。ODRA还整合了阻抗协调器以解决患者顺从性问题，该协调器采用引导技术引出与其阻抗水平相符的行为。自动与专家评估显示，ODRA在治疗技能、CBT一致性和患者行为保真度方面显著优于现有方法，持证心理学家在13项临床指标中有12项更偏好ODRA生成的会话。此外，在我们的数据集上微调的模型，相较于合作型与阻抗型患者均表现出更优的治疗性能，验证了合成训练数据中明确的阻抗建模可直接转化为下游临床稳健性。
英文摘要
Synthetic generation of Cognitive Behavioral Therapy (CBT) sessions is challenged by two competing demands: adhering to strict therapeutic structure while modeling the resistant, unpredictable behavior of real patients. Existing script-based methods fail to capture dynamic therapeutic interactions, while multi-agent approaches struggle to adhere to CBT's sequential structure; both suffer from sycophancy, producing overly compliant patients that misrepresent real clinical settings. In this work we introduce ODRA, a novel framework for synthesizing therapy dialogues through a Chain-of-Thought (CoT) strategy grounded in foundational CBT guidelines (Beck, 2020). ODRA further incorporates a resistance orchestrator to solve patient sycophancy, which employs steering techniques to elicit behaviors aligned with their resistance level. Automated and expert evaluations show that ODRA significantly outperforms existing methods across therapeutic skills, CBT alignment, and patient behavioral fidelity, with licensed psychologists preferring ODRA sessions across 12 of 13 clinical metrics. Furthermore, models fine-tuned on our dataset demonstrate superior therapeutic performance against both cooperative and resistant patients, validating that explicit resistance modeling in synthetic training data directly translates to downstream clinical robustness.
Comments39 pages, 23 figures, 12 tables
