AI Realtor: Towards Grounded Persuasive Language Generation for Automated Copywriting

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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.