Title: AI Realtor: Towards Grounded Persuasive Language Generation for Automated Copywriting
Primary area: foundation or frontier models, including LLMs
Keywords: Persuasive Language Generation, Large Language Model, Automated Copywriting

Abstract: 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.

TL;DR: To what extent LLMs can generate grounded, persuasive language for automated copywriting