AI Realtor: Towards Grounded Persuasive Language Generation for Automated Copywriting
This addresses the problem of automating targeted copywriting for real estate marketing, offering an incremental improvement by combining existing modules for grounding, personalization, and marketing.
The paper tackled generating persuasive real estate marketing copy by developing an agentic LLM framework that aligns content with user preferences and factual attributes, resulting in descriptions preferred over human experts by a clear margin while maintaining factual accuracy.
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.