发表机构
Google DeepMind（谷歌DeepMind）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过五天992人实验，发现个性化语言模型主要影响用户自我披露、感知及后悔等态度行为，而非核心互动变化，为个性化AI设计提供启示。
AI 中文摘要
开发个性化语言模型的兴趣正在迅速增长。虽然个性化通常被视为更好地满足多样化用户需求的一种机制，但持续与个性化模型互动对人们对人工智能的感知和行为的影响仍鲜为人知。最关键的是，直接人机互动循环之外的后续后果，例如对用户自我认知和人际关系的影响，在很大程度上仍未得到检验。在本研究中，我们招募了992名参与者在五天时间内与语言模型完成每日寻求建议的互动，将非个性化基线的结果与两种个性化方法进行比较：基于记忆的（以先前的对话历史为条件）和基于调查的（以通过研究前摄入调查收集的信息为条件）。我们发现，人机互动随时间推移的若干变化主要由重复接触驱动，而非个性化本身。然而，与个性化模型互动的参与者在寻求建议和信息分享的态度与行为上经历了差异：基于记忆条件下的参与者进行了更多的自我披露，并认为模型不那么令人毛骨悚然，而基于调查条件下的参与者报告了更多关于与人工智能分享个人信息的后悔。最后，我们强调了不同个性化方法对互动结果的细微影响，并讨论了这些发现对负责任设计和部署个性化人工智能系统的意义。
英文摘要
Interest in developing personalised language models is rapidly growing. While personalisation is often viewed as a mechanism to better serve diverse user needs, the effects of sustained interactions with personalised models on people's perception of and behaviour toward AI remain poorly understood. Most critically, downstream consequences outside the immediate human--AI interaction loop, such as effects on users' self-perceptions and interpersonal relationships, remain largely unexamined. In this study, we recruited 992 participants to complete daily advice-seeking interactions with language models over the course of five days, comparing outcomes from a non-personalised baseline against two personalisation approaches: memory-based (conditioned on prior conversational history) and survey-based (conditioned on information collected through a pre-study intake survey). We find that several changes in human-AI interaction over time are driven primarily by repeated exposure rather than personalisation itself. However, participants interacting with personalised models experienced differences in advice-seeking and information-sharing attitudes and behaviours: participants in the memory-based condition engaged in greater self-disclosure and rated the model as less creepy, while participants in the survey-based condition reported higher regret about having shared personal information with the AI. We conclude by highlighting the nuanced effects of different personalisation approaches on interaction outcomes, and discussing the implications of these findings for the responsible design and deployment of personalised AI systems.
