# PushGen: Push Notifications Generation with LLM

arXiv: https://arxiv.org/abs/2512.14490 · HTML: https://mlsanity.com/paper/2512.14490/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2025.

## Built on by (most cited first, 2 of 2)

- [Sustained Impact of Agentic Personalisation in Marketing: A Longitudinal Case Study](https://mlsanity.com/paper/2604.08621/) (2026)
- [LLM-Based Intelligent Notification Composition: From Static Personalization to Context-Aware Persuasive Messaging](https://mlsanity.com/paper/2605.16264/) (2026)

## Abstract (arXiv)

We present PushGen, an automated framework for generating high-quality push notifications comparable to human-crafted content. With the rise of generative models, there is growing interest in leveraging LLMs for push content generation. Although LLMs make content generation straightforward and cost-effective, maintaining stylistic control and reliable quality assessment remains challenging, as both directly impact user engagement. To address these issues, PushGen combines two key components: (1) a controllable category prompt technique to guide LLM outputs toward desired styles, and (2) a reward model that ranks and selects generated candidates. Extensive offline and online experiments demonstrate its effectiveness, which has been deployed in large-scale industrial applications, serving hundreds of millions of users daily.