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。