让AI人物角色基于真实用户数据回应，确保可验证性。
PersonaCite: VoC-Grounded Interviewable Agentic Synthetic AI Personas for Verifiable User and Design Research
- 用检索增强交互，让角色回答来自真实用户资料
- 缺失证据时主动拒绝回答，避免编造内容
- 提供溯源卡片，适合设计研究中的可信协作
基于大模型和代理的合成人物在设计与产品决策中日益普及，但现有方法依赖提示词扮演，常产生有说服力却不可验证的回复，掩盖其依据。我们提出PersonaCite，一种以检索增强交互重构AI人物为证据约束的研究工具。不同于传统提示扮演，PersonaCite在每轮对话中检索真实用户之声（VoC）素材，将回应限制于所获证据，证据缺失时明确拒答，并提供逐条响应的来源标注。通过14位行业专家的半结构化访谈与部署研究，我们识别出感知收益、有效性担忧与设计张力等初步发现，并提出Persona Provenance Cards作为负责任使用AI人物在以人为中心设计流程中的文档范式。
原文摘要 · Abstract (English)
LLM-based and agent-based synthetic personas are increasingly used in design and product decision-making, yet prior work shows that prompt-based personas often produce persuasive but unverifiable responses that obscure their evidentiary basis. We present PersonaCite, an agentic system that reframes AI personas as evidence-bounded research instruments through retrieval-augmented interaction. Unlike prior approaches that rely on prompt-based roleplaying, PersonaCite retrieves actual voice-of-customer artifacts during each conversation turn, constrains responses to retrieved evidence, explicitly abstains when evidence is missing, and provides response-level source attribution. Through semi-structured interviews and deployment study with 14 industry experts, we identify preliminary findings on perceived benefits, validity concerns, and design tensions, and propose Persona Provenance Cards as a documentation pattern for responsible AI persona use in human-centered design workflows.
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