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.

翻译：本文开发了一种基于大语言模型（LLM）的智能体框架，用于自动化文案撰写中基于事实的说服性语言生成，并以房地产营销为重点应用场景。我们的方法旨在使生成内容既符合用户偏好，又能突出有用的客观属性特征。该智能体包含三个核心模块：（1）事实基础模块，模拟人类专家行为以预测具有市场价值的房产特征；（2）个性化模块，使内容与用户偏好对齐；（3）营销模块，确保事实准确性并融入本地化特征。我们在房地产营销领域进行了系统性的人体实验，研究对象为潜在购房者群体。实验结果表明，相较于人类专家撰写的营销文案，本方法生成的描述在保持同等事实准确性的前提下，获得了显著更高的偏好度。我们的研究结果表明，这种智能体方法在确保内容事实性的同时，为大规模定向文案自动化生成提供了可行路径。