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

arXiv: https://arxiv.org/abs/2502.03804 · HTML: https://mlsanity.com/paper/2502.03804/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 87 references that are in the map.
- First posted on arXiv 6 Feb 2025.

## Builds on (most cited first, 19 of 19)

- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT](https://mlsanity.com/paper/2303.04226/) (2023)
- [A Survey on Conversational Recommender Systems](https://mlsanity.com/paper/2004.00646/) (2020)
- [Gmail Smart Compose: Real-Time Assisted Writing](https://mlsanity.com/paper/1906.00080/) (2019)
- [The Impact of Multiple Parallel Phrase Suggestions on Email Input and Composition Behaviour of Native and Non-Native English Writers](https://mlsanity.com/paper/2101.09157/) (2021)
- [Smart Reply: Automated Response Suggestion for Email](https://mlsanity.com/paper/1606.04870/) (2016)
- [Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models](https://mlsanity.com/paper/2402.11723/) (2024)
- [Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4](https://mlsanity.com/paper/2312.16171/) (2023)
- [LaMPost: Design and Evaluation of an AI-assisted Email Writing Prototype for Adults with Dyslexia](https://mlsanity.com/paper/2207.02308/) (2022)
- ["If the Machine Is As Good As Me, Then What Use Am I?" -- How the Use of ChatGPT Changes Young Professionals' Perception of Productivity and Accomplishment](https://mlsanity.com/paper/2404.12549/) (2024)
- [From Text to Self: Users' Perceptions of Potential of AI on Interpersonal Communication and Self](https://mlsanity.com/paper/2310.03976/) (2023)
- ["AI enhances our performance, I have no doubt this one will do the same": The Placebo effect is robust to negative descriptions of AI](https://mlsanity.com/paper/2309.16606/) (2023)
- [Comparing Sentence-Level Suggestions to Message-Level Suggestions in AI-Mediated Communication](https://mlsanity.com/paper/2302.13382/) (2023)
- [Toward Extractive Summarization of Online Forum Discussions via Hierarchical Attention Networks](https://mlsanity.com/paper/1805.10390/) (2018)
- [LLM-based Smart Reply (LSR): Enhancing Collaborative Performance with ChatGPT-mediated Smart Reply System](https://mlsanity.com/paper/2306.11980/) (2023)
- [Evolution of Conversations in the Age of Email Overload](https://mlsanity.com/paper/1504.00704/) (2015)
- [AINeedsPlanner: A Workbook to Support Effective Collaboration Between AI Experts and Clients](https://mlsanity.com/paper/2402.08938/) (2024)
- [On (Commercial) Benefits of Automatic Text Summarization Systems in the News Domain: A Case of Media Monitoring and Media Response Analysis](https://mlsanity.com/paper/1701.00728/) (2017)
- [CrowdTone: Crowd-powered tone feedback and improvement system for emails](https://mlsanity.com/paper/1701.01793/) (2017)

## Built on by (most cited first, 6 of 6)

- [BenchPreS: A Benchmark for Context-Aware Personalized Preference Selectivity of Persistent-Memory LLMs](https://mlsanity.com/paper/2603.16557/) (2026)
- [Not My Truce: Personality Differences in AI-Mediated Workplace Negotiation](https://mlsanity.com/paper/2604.00464/) (2026)
- [StepWrite: Adaptive Planning for Speech-Driven Text Generation](https://mlsanity.com/paper/2508.04011/) (2025)
- [User Perceptions vs. Proxy LLM Judges: Privacy and Helpfulness in LLM Responses to Privacy-Sensitive Scenarios](https://mlsanity.com/paper/2510.20721/) (2025)
- [LLMs Should Incorporate Explicit Mechanisms for Human Empathy](https://mlsanity.com/paper/2604.10557/) (2026)
- [Fast and Accurate: Improving Personalized LLM Performance by Semantic Concentration Inspired Workload Split between RAG and Fine-Tuning](https://mlsanity.com/paper/2610.15921/) (2026)

## Abstract (arXiv)

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.