分析14万次真实对话，发现用户与大模型互动模式早早就定型。
Priming, Path-dependence, and Plasticity: Understanding the molding of user-LLM interaction and its implications from (many) chat logs in the wild
- 通过分析7955名用户的长期对话记录，发现交互模式在早期迅速形成并稳定。
- 早期探索行为与长期文本重复率和留存率高度相关，证明路径依赖显著。
- 适合关注用户体验设计、人机交互长期演化的研究者参考。
用户与大模型的交互受过往经验与个体探索影响，但实验室研究无法揭示真实世界中的这些因素。本文通过分析来自全球7,955名匿名用户的14万次聊天会话，揭示了尽管任务各异，用户表达仍存在关键模式：(1) 用户并非白板，也非持续适应；交互模式通过早期轨迹快速形成并稳定；(2) 长期结果（如重复文本模式、留存率）与早期探索强相关；(3) 存在并行动态，如按任务类型（如情感支持）组织表达，或响应模型版本更新。结果呈现“能动性悖论”：尽管输入空间无限自由，用户实际探索却减少。研究呼吁在设计中考虑互动塑形过程，并融入未来研究。
原文摘要 · Abstract (English)
User interactions with LLMs are shaped by prior experiences and individual exploration, but in-lab studies do not provide system designers with visibility into these in-the-wild factors. This work explores a new approach to studying real-world user-LLM interactions through large-scale chat logs from the wild. Through analysis of 140K chatbot sessions from 7,955 anonymized global users over time, we demonstrate key patterns in user expressions despite varied tasks: (1) LLM users are not tabula rasa, nor are they constantly adapting; rather, interaction patterns form and stabilize rapidly through individual early trajectories; (2) Longitudinal outcomes, such as recurring text patterns and retention rates, are strongly correlated with early exploration; (3) Parallel dynamics are present, including organizing expressions by task types such as emotional support, or in response to model-version updates. These results present an ``agency paradox'': despite LLM input spaces being unconstrained and user-driven, we in fact see less user exploration. We call for design consideration surrounding the molding procedure and its incorporation in future research.
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