This paper develops an agentic framework that employs large language models (LLMs) for grounded persuasive language generation in automated copywriting, with real estate marketing as a focal application. Our method is designed to align the generated content with user preferences while highlighting useful factual attributes. This agent consists of three key modules: (1) Grounding Module, mimicking expert human behavior to predict marketable features; (2) Personalization Module, aligning content with user preferences; (3) Marketing Module, ensuring factual accuracy and the inclusion of localized features. We conduct systematic human-subject experiments in the domain of real estate marketing, with a focus group of potential house buyers. The results demonstrate that marketing descriptions generated by our approach are preferred over those written by human experts by a clear margin while maintaining the same level of factual accuracy. Our findings suggest a promising agentic approach to automate large-scale targeted copywriting while ensuring factuality of content generation.
@article{arxiv.2502.16810,
title = {AI Realtor: Towards Grounded Persuasive Language Generation for Automated Copywriting},
author = {Jibang Wu and Chenghao Yang and Yi Wu and Simon Mahns and Chaoqi Wang and Hao Zhu and Fei Fang and Haifeng Xu},
journal= {arXiv preprint arXiv:2502.16810},
year = {2026}
}