# PersonaMail: Learning and Adapting Personal Communication Preferences for Context-Aware Email Writing

arXiv: https://arxiv.org/abs/2602.17340 · HTML: https://mlsanity.com/paper/2602.17340/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 9 of its 96 references that are in the map.
- First posted on arXiv 19 Feb 2026.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Human values and AI feedback alignment](https://mlsanity.com/alignment/human-values-and-ai-feedback-alignment/)

## Builds on (most cited first, 9 of 9)

- [Does Writing with Language Models Reduce Content Diversity?](https://mlsanity.com/paper/2309.05196/) (2023)
- [Do LLMs write like humans? Variation in grammatical and rhetorical styles](https://mlsanity.com/paper/2410.16107/) (2024)
- [From Text to Transformation: A Comprehensive Review of Large Language Models' Versatility](https://mlsanity.com/paper/2402.16142/) (2024)
- [From Text to Self: Users' Perceptions of Potential of AI on Interpersonal Communication and Self](https://mlsanity.com/paper/2310.03976/) (2023)
- [Corporate Communication Companion (CCC): An LLM-empowered Writing Assistant for Workplace Social Media](https://mlsanity.com/paper/2405.04656/) (2024)
- [Textoshop: Interactions Inspired by Drawing Software to Facilitate Text Editing](https://mlsanity.com/paper/2409.17088/) (2024)
- [AI-Instruments: Embodying Prompts as Instruments to Abstract & Reflect Graphical Interface Commands as General-Purpose Tools](https://mlsanity.com/paper/2502.18736/) (2025)
- [Should LLMs be WEIRD? Exploring WEIRDness and Human Rights in Large Language Models](https://mlsanity.com/paper/2508.19269/) (2025)
- [Content-Driven Local Response: Supporting Sentence-Level and Message-Level Mobile Email Replies With and Without AI](https://mlsanity.com/paper/2502.06430/) (2025)

## Built on by (most cited first, 1 of 1)

- [Towards Cognitive Process-Aware Proactive Writing Support](https://mlsanity.com/paper/2608.30424/) (2026)

## Abstract (arXiv)

LLM-assisted writing has seen rapid adoption in interpersonal communication, yet current systems often fail to capture the subtle tones essential for effectiveness. Email writing exemplifies this challenge: effective messages require careful alignment with intent, relationship, and context beyond mere fluency. Through formative studies, we identified three key challenges: articulating nuanced communicative intent, making modifications at multiple levels of granularity, and reusing effective tone strategies across messages. We developed PersonaMail, a system that addresses these gaps through structured communication factor exploration, granular editing controls, and adaptive reuse of successful strategies. Our evaluation compared PersonaMail against standard LLM interfaces, and showed improved efficiency in both immediate and repeated use, alongside higher user satisfaction. We contribute design implications for AI-assisted communication systems that prioritize interpersonal nuance over generic text generation.