LLMs are now embedded in a wide range of everyday scenarios. However, their inherent hallucinations risk hiding misinformation in fluent responses, raising concerns about overreliance on AI. Detecting overreliance is challenging, as it often arises in complex, dynamic contexts and cannot be easily captured by post-hoc task outcomes. In this work, we aim to investigate how users' behavioral patterns correlate with overreliance. We collected interaction logs from 77 participants working with an LLM injected plausible misinformation across three real-world tasks and we assessed overreliance by whether participants detected and corrected these errors. By semantically encoding and clustering segments of user interactions, we identified five behavioral patterns linked to overreliance: users with low overreliance show careful task comprehension and fine-grained navigation; users with high overreliance show frequent copy-paste, skipping initial comprehension, repeated LLM references, coarse locating, and accepting misinformation despite hesitation. We discuss design implications for mitigation.

翻译：大型语言模型现已广泛应用于各类日常场景。然而，其固有的幻觉风险可能将错误信息隐藏在流畅的回复中，引发对人工智能过度依赖的担忧。检测过度依赖具有挑战性，因为它常产生于复杂动态的交互情境，难以通过事后任务结果简单捕捉。本研究旨在探究用户行为模式与过度依赖之间的关联。我们收集了77名参与者在三项现实任务中与植入可信错误信息的LLM的交互日志，并通过参与者是否发现并纠正这些错误来评估其过度依赖程度。通过对用户交互片段进行语义编码与聚类分析，我们识别出五种与过度依赖相关的行为模式：低度依赖用户表现出细致的任务理解与细粒度导航；高度依赖用户则呈现频繁复制粘贴、跳过初始理解、反复查阅LLM回复、粗粒度定位以及在犹豫状态下仍接受错误信息等特征。最后，我们探讨了缓解此类问题的设计启示。