六款大模型戒烟建议审计：回复内容因用户人设不同而变化
Same Question, Different Answers: Auditing Persona-Dependent Variation in Six LLMs on Smoking-Cessation Advice
研究者拿六款大模型测戒烟建议，发现换个提问人设答案就变，青少年拿到的处方药信息少很多，还公开了全部数据。
一项审计让四个厂商的六款 LLM 面对十二种用户人设回答相同的四个戒烟问题，共收集 2,880 条回复。结果显示所有模型对青少年人设提及 FDA 处方戒烟药物的比例比对成人人设低 32 到 67 个百分点，六款中有五款对自称有医疗专业知识的用户给出的医学事实更少。测试用一条规定事实底线的系统提示词收窄人设差异，但其对事实覆盖率的影响在不同模型间方向不一，提示词无法跨模型直接复用。作者提醒处方戒烟药物本不推荐给未成年人，事实底线需按年龄区分而非一刀切。全部 2,880 条回复与分析代码已公开。
Same Question, Different Answers: Auditing Persona-Dependent Variation in Six LLMs on Smoking-Cessation Advice
Hundreds of millions of people now ask large language model (LLM) chatbots for health advice, which raises a question that is simple to pose and hard to answer: does everyone get the same advice? We audit six widely deployed LLMs from four providers on smoking cessation, a domain with codified clini- cal guidance and substantial public-health stakes, by putting the same four cessation questions to twelve user personas that differ in life stage, self-described expertise, and smoking history, for a total of 2,880 responses. What the models say depends on who appears to be asking. Every model named FDA-approved prescription cessation medications less often to teen personas than to adult personas, by 32 to 67 percentage points, and in five of six models a persona presenting health- care expertise received fewer facts than a persona presenting none. We then evaluate one deployable intervention: a short system prompt specifying a factual floor, the set of core med- ical facts every user should receive when they are relevant, while leaving tone, vocabulary, and framing free to adapt. The prompt narrows persona differences in every model, but its effect on overall fact coverage varies in both magnitude and direction across models, so a prompt validated on one system cannot be assumed to transfer to another. Two cautions frame these results. Equal mention rates are not always the correct clinical target, because prescription cessation pharmacother- apy is not recommended for minors, so a defensible factual floor must be age-aware rather than uniform. And because our personas vary on several attributes at once, we report scenario- level differences rather than isolated effects of age or expertise. We release all 2,880 responses and the analysis code.