基于场景的说服性语言生成在自动化营销中的应用

Grounded Persuasive Language Generation for Automated Marketing

February 24, 2025

作者: Jibang Wu, Chenghao Yang, Simon Mahns, Chaoqi Wang, Hao Zhu, Fei Fang, Haifeng Xu

cs.AI

摘要

本文提出了一种基于大语言模型（LLMs）的智能框架，旨在自动生成具有说服力且基于事实的营销内容，并以房地产房源描述作为核心应用领域。该方法旨在使生成的内容既符合用户偏好，又能突出有用的实际属性。该智能体包含三个关键模块：（1）基础模块，模拟专家行为以预测市场关注的特征；（2）个性化模块，确保内容与用户偏好相匹配；（3）营销模块，保证事实准确性并融入本地化特色。我们在房地产营销领域进行了系统性的人体实验，以潜在购房者为焦点小组。结果表明，相较于人类专家撰写的描述，采用本方法生成的营销描述明显更受青睐。我们的研究发现，这一基于LLM的智能框架在实现大规模定向营销自动化的同时，能够确保仅使用事实进行负责任的生成，展现出广阔的应用前景。

English

This paper develops an agentic framework that employs large language models (LLMs) to automate the generation of persuasive and grounded marketing content, using real estate listing descriptions as our focal application domain. 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. Our findings suggest a promising LLM-based agentic framework to automate large-scale targeted marketing while ensuring responsible generation using only facts.