用AI提问辅助回复正式邮件，省时又省力。
Understanding and Supporting Formal Email Exchange by Answering AI-Generated Questions
- 用户只需回答系统生成的简短问题，无需自写复杂提示。
- 实验显示效率提升，工作量降低，邮件质量不变。
- 适合需要频繁处理正式邮件的人群使用。
回复正式邮件耗时且费脑力，需兼顾礼貌措辞与内容回应。尽管已有基于大语言模型（LLM）的系统简化此过程，但用户仍需提供详细提示才能获得理想回复。为此，我们提出并评估了一种基于LLM的问答（QA）方法：通过分析收到的邮件生成一组简短问题，用户作答后自动生成回复。我们开发了原型系统ResQ，进行了控制实验（12名参与者）和实地实验（8名参与者）。结果表明，相比需手动设计提示的传统方法，该问答方法显著提升了邮件回复效率，降低了认知负荷，同时保持了邮件质量。我们还探讨了该方法对回复流程及人际互动的影响，以及在人工智能中介沟通中使用问答方式的机遇与挑战。
原文摘要 · Abstract (English)
Replying to formal emails is time-consuming and cognitively demanding, as it requires crafting polite phrasing and providing an adequate response to the sender's demands. Although systems with Large Language Models (LLMs) were designed to simplify the email replying process, users still need to provide detailed prompts to obtain the expected output. Therefore, we proposed and evaluated an LLM-powered question-and-answer (QA)-based approach for users to reply to emails by answering a set of simple and short questions generated from the incoming email. We developed a prototype system, ResQ, and conducted controlled and field experiments with 12 and 8 participants. Our results demonstrated that the QA-based approach improves the efficiency of replying to emails and reduces workload while maintaining email quality, compared to a conventional prompt-based approach that requires users to craft appropriate prompts to obtain email drafts. We discuss how the QA-based approach influences the email reply process and interpersonal relationship dynamics, as well as the opportunities and challenges associated with using a QA-based approach in AI-mediated communication.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。