Understanding and Supporting Formal Email Exchange by Answering AI-Generated Questions

2025-02-06

AI Summary

Formal email reply generation is time-consuming, imposes high cognitive load, and critically depends on users’ ability to craft effective prompts. Method: This paper proposes a question-answering (QA)-driven large language model (LLM) interaction paradigm: the system automatically parses incoming emails to extract semantic intent and generates concise, structured questions; users answer these questions, and the system synthesizes a contextually appropriate, professional reply. Contribution/Results: This work pioneers the decomposition of high-level text generation into accessible, low-threshold QA interactions—eliminating the need for manual prompt engineering. Controlled experiments and field studies demonstrate that the approach significantly improves response efficiency and substantially reduces cognitive load, while maintaining parity with conventional prompt-based methods in politeness, content completeness, and domain-specific professionalism.

Technology Category

• Natural Language Processing: Question Answering
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Replying to formal emails is time-consuming and cognitively demanding, as it requires polite phrasing and ensuring an adequate response to the sender's demands. Although systems with Large Language Models (LLM) were designed to simplify the email replying process, users still needed 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 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 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.

Problem

• Enhances email reply efficiency
• Reduces cognitive workload
• Maintains email quality

Innovation

• LLM-powered QA-based email reply
• Generates simple short questions
• Improves efficiency reduces workload