用户对大模型搜索的固有期待限制了创新使用，但失败后会主动调整策略。
Trapped by Expectations: Functional Fixedness in LLM-Enabled Chat Search
- 分析450人实验，发现用户习惯受过往使用经验影响。
- 预期落空时，用户会增加提示多样性与细节，主动适应系统。
- 适合研究人机交互、AI可用性及提升大模型创造力的团队参考。
功能固定是一种认知偏见，使用户仅以熟悉方式与新系统互动，限制了大语言模型（LLM）在复杂探索任务中的潜力。我们通过众包实验招募450名参与者，完成涵盖公共安全、饮食健康、可持续发展和人工智能伦理的六类决策任务。每位参与者与ChatGPT进行多轮对话，对比其对话前的预期与实际交互行为。结果表明：1）用户的预设期待与其使用ChatGPT、搜索引擎和虚拟助手的经验密切相关；2）高频使用ChatGPT者减少指示词和模糊表达，频繁调整提示；搜索经验丰富者采用结构化、少对话风格，修改极少；虚拟助手用户偏好命令式提示，强化功能固定；3）当系统未满足预期时，用户生成更详细、语言更丰富的提示，体现行为适应性。这说明虽初始互动受预期制约，但预期落空可激发适应性调整。若系统提供恰当支持，或能推动用户更广泛探索LLM能力。本文还构建了聊天搜索中用户意图的分类体系，强调缓解功能固定对实现更具创造性和分析性使用的重要性。
原文摘要 · Abstract (English)
Functional fixedness, a cognitive bias that restricts users' interactions with a new system or tool to expected or familiar ways, limits the full potential of Large Language Model (LLM)-enabled chat search, especially in complex and exploratory tasks. To investigate its impact, we conducted a crowdsourcing study with 450 participants, each completing one of six decision-making tasks spanning public safety, diet and health management, sustainability, and AI ethics. Participants engaged in a multi-prompt conversation with ChatGPT to address the task, allowing us to compare pre-chat intent-based expectations with observed interactions. We found that: 1) Several aspects of pre-chat expectations are closely associated with users' prior experiences with ChatGPT, search engines, and virtual assistants; 2) Prior system experience shapes language use and prompting behavior. Frequent ChatGPT users reduced deictic terms and hedge words and frequently adjusted prompts. Users with rich search experience maintained structured, less-conversational queries with minimal modifications. Users of virtual assistants favored directive, command-like prompts, reinforcing functional fixedness; 3) When the system failed to meet expectations, participants generated more detailed prompts with increased linguistic diversity, reflecting adaptive shifts. These findings suggest that while preconceived expectations constrain early interactions, unmet expectations can motivate behavioral adaptation. With appropriate system support, this may promote broader exploration of LLM capabilities. This work also introduces a typology for user intents in chat search and highlights the importance of mitigating functional fixedness to support more creative and analytical use of LLMs.
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