用户与AI在模型升级中存在双向期待，研究揭示了这种心理动态的实证规律。
Mutual Wanting in Human--AI Interaction: Empirical Evidence from Large-Scale Analysis of GPT Model Transitions
- 用双算法主题建模分析用户评论，识别出用户对AI的期待模式
- 近半数用户使用拟人化语言，信任表达远超背叛情绪
- 提出可量化的'互欲对齐'框架，适用于AI体验优化设计
大语言模型的快速迭代催生了用户与AI系统间复杂的双向期待，但这些期待机制尚不清晰。本文引入“互欲”概念，通过分析主流AI论坛的用户评论及多轮OpenAI模型的受控实验，首次实现大规模实证验证人类-人工智能互动中的双向欲望动态。研究发现，近一半用户使用拟人化语言，信任表达显著高于背叛性表述，且用户可划分为若干“互欲类型”。我们识别出可测量的期望落差模式，并量化了重大模型发布后的期望-现实差距。基于双重算法主题建模与多维特征提取技术，构建了“互欲对齐框架”（M-WAF），为前瞻性用户体验管理与AI系统设计提供实践工具。研究证实‘互欲’是可测量现象，对构建更可信、具关系感知的AI系统具有明确启示。
原文摘要 · Abstract (English)
The rapid evolution of large language models (LLMs) creates complex bidirectional expectations between users and AI systems that are poorly understood. We introduce the concept of "mutual wanting" to analyze these expectations during major model transitions. Through analysis of user comments from major AI forums and controlled experiments across multiple OpenAI models, we provide the first large-scale empirical validation of bidirectional desire dynamics in human-AI interaction. Our findings reveal that nearly half of users employ anthropomorphic language, trust significantly exceeds betrayal language, and users cluster into distinct "mutual wanting" types. We identify measurable expectation violation patterns and quantify the expectation-reality gap following major model releases. Using advanced NLP techniques including dual-algorithm topic modeling and multi-dimensional feature extraction, we develop the Mutual Wanting Alignment Framework (M-WAF) with practical applications for proactive user experience management and AI system design. These findings establish mutual wanting as a measurable phenomenon with clear implications for building more trustworthy and relationally-aware AI systems.
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