Recent works leverage LLMs to roleplay realistic social scenarios, aiding novices in practicing their social skills. However, simulating sensitive interactions, such as in mental health, is challenging. Privacy concerns restrict data access, and collecting expert feedback, although vital, is laborious. To address this, we develop Roleplay-doh, a novel human-LLM collaboration pipeline that elicits qualitative feedback from a domain-expert, which is transformed into a set of principles, or natural language rules, that govern an LLM-prompted roleplay. We apply this pipeline to enable senior mental health supporters to create customized AI patients for simulated practice partners for novice counselors. After uncovering issues in GPT-4 simulations not adhering to expert-defined principles, we also introduce a novel principle-adherence prompting pipeline which shows 30\% improvements in response quality and principle following for the downstream task. Via a user study with 25 counseling experts, we demonstrate that the pipeline makes it easy and effective to create AI patients that more faithfully resemble real patients, as judged by creators and third-party counselors.

我们开发了一个新颖的人工智能-语言模型协作流程，通过从专家获取定性反馈并将其转化为自然语言规则来指导语言模型模拟角色扮演，从而帮助初学者练习心理健康方面的社交技巧。通过与25名咨询专家进行用户研究，我们证明该流程使得创建更贴近真实患者的人工智能患者变得轻松有效。

角色扮演者：通过获得和坚持原则使领域专家创建 LLM 模拟患者