Alignment research on large language models (LLMs) increasingly depends on understanding how these systems are used in everyday contexts. yet naturalistic interaction data is difficult to access due to privacy constraints and platform control. We present AI-Wrapped, a prototype workflow for collecting naturalistic LLM usage data while providing participants with an immediate ``wrapped''-style report on their usage statistics, top topics, and safety-relevant behavioral patterns. We report findings from an initial deployment with 82 U.S.-based adults across 48,495 conversations from their 2025 histories. Participants used LLMs for both instrumental and reflective purposes, including creative work, professional tasks, and emotional or existential themes. Some usage patterns were consistent with potential over-reliance or perfectionistic refinement, while heavier users showed comparatively more reflective exchanges than primarily transactional ones. Methodologically, even with zero data retention and PII removal, participants may remain hesitant to share chat data due to perceived privacy and judgment risks, underscoring the importance of trust, agency, and transparent design when building measurement infrastructure for alignment research.

翻译：大型语言模型（LLM）的对齐研究日益依赖于理解这些系统在日常情境中的使用方式。然而，由于隐私限制和平台控制，自然交互数据难以获取。本文提出AI-Wrapped——一种用于收集自然主义LLM使用数据的原型工作流，同时为参与者即时提供其使用统计、热门话题及安全相关行为模式的“年度总结”式报告。我们报告了首次部署的研究结果：该部署涉及82名美国成年人，涵盖其2025年历史中的48,495次对话。参与者将LLM用于工具性和反思性目的，包括创意工作、专业任务以及情感或存在主义主题。部分使用模式显示出潜在的过度依赖或完美主义优化倾向，而重度用户相比主要事务性对话，表现出更多反思性交流。在方法论层面，即使采用零数据保留和个人身份信息移除措施，参与者仍可能因感知到的隐私与评判风险而对分享聊天数据持犹豫态度，这凸显了在构建对齐研究测量基础设施时信任、自主权和透明设计的重要性。