AI 中文总结
本研究分析8.4万次真实人机对话，发现响应特征与认知困难的关系取决于组合方式，提出对话复杂度预算概念，以指导针对用户、任务和交互的响应复杂度配置。
AI 中文摘要
生成式AI日益支持复杂的金融和健康决策，然而，我们对其在真实世界对话中何时变得难以处理知之甚少。我们分析了超过84,000次ChatGPT和Gemini对话，将重复提示和误解后的澄清作为认知困难的的行为指标。我们发现，诸如长度、可读性和词汇多样性等响应特征与对话困难之间并不存在固定关系；相反，它们的关系取决于这些特征如何组合。最值得注意的是，在较短的响应中，更高的词汇多样性与较少的重复提示相关，但随着响应长度的增加，这种关联减弱，这一模式在金融和健康对话中均得到复现。我们提出了一个对话复杂度预算的概念来概念化这些相互依赖性：与某一响应特征相关的需求可能取决于伴随它的其他特征。由此产生的设计挑战是如何针对特定用户、任务和交互来配置响应复杂度。
英文摘要
Generative AI increasingly supports complex financial and health decisions, yet we know little about when its responses become difficult to process in real-world dialogue. We analyse more than 84,000 ChatGPT and Gemini conversations, using repeated prompting and clarification following misunderstanding as behavioural indicators of cognitive difficulty. We find that response characteristics such as length, readability and lexical diversity do not have fixed relationships with conversational difficulty; instead, their relationships depend on how they combine. Most notably, greater lexical diversity was associated with less repeated prompting in shorter responses, but this association weakened as response length increased, a pattern that replicated across financial and health conversations. We propose a conversational complexity budget to conceptualise these interdependencies: the demands associated with one response characteristic may depend on those accompanying it. The resulting design challenge is how to configure response complexity for the particular user, task and interaction.
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