Ask ChatGPT about vacation planning, and it may infer your income. Ask it about medication, and it may infer your medical history. Because such inferences can expose more information than users intend to reveal, prior work argues that they are a defining privacy risk of LLM-based systems. Yet prior work has mostly shown that LLMs can make potentially violating inferences, not how users experience those inferences nor what controls users may want governing their use. We built the Reflective Layer, a visualization tool that surfaces example unstated inferences from users' own ChatGPT histories, and used it in a mixed-methods study with 18 regular ChatGPT users evaluating 215 surfaced inferences from their own conversations. Counterintuitively, participants reacted more strongly with curiosity and interest rather than distress and concern. Discomfort arose mainly when inferences felt misrepresentative of the user or misaligned with expected use. Participants were also markedly less comfortable with advertisers and third-party applications using those inferences than with platform providers. These findings suggest that the acceptability of LLM inferences is governed not only by its content, but by context-sensitive norms around how they are generated, retained within the platform, and transmitted beyond it.

翻译：向ChatGPT咨询度假计划，它可能推断出你的收入；询问用药信息，它可能推断出你的病史。由于此类推理可能暴露超出用户意愿的信息，先前研究认为这是基于LLM系统特有的隐私风险。然而，现有研究主要表明LLM能进行潜在侵犯性的推理，并未揭示用户对此类推理的真实体验，也未探讨用户希望如何管控这些推理的使用。我们构建了"反思层"可视化工具，可呈现用户自身ChatGPT历史记录中未明示的推理示例，并通过混合研究方法对18名常规ChatGPT用户展开调研，评估了来自其对话记录的215条推理案例。出乎意料的是，参与者主要表现出好奇与兴趣，而非困扰与担忧。不适感主要源于推理结果未能准确代表用户或偏离预期使用场景。相较于平台提供商，参与者对广告商及第三方应用使用此类推理的接受度显著更低。这些发现表明，LLM推理的可接受性不仅取决于推理内容，更受限于关于推理如何生成、如何在平台内留存及如何向外部传递的情境化规范。