基於實證的勸說性語言生成在自動化行銷中的應用

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

本文開發了一個基於大型語言模型（LLMs）的代理框架，旨在自動生成具有說服力且基於事實的營銷內容，並以房地產列表描述作為主要應用領域。我們的方法旨在使生成的內容與用戶偏好保持一致，同時突出有用的實際屬性。該代理由三個關鍵模塊組成：（1）基礎模塊，模仿專家行為以預測具有市場價值的特徵；（2）個性化模塊，使內容與用戶偏好對齊；（3）營銷模塊，確保事實準確性並包含本地化特徵。我們在房地產營銷領域進行了系統性的人體實驗，目標群體為潛在購房者。結果表明，與人類專家撰寫的營銷描述相比，我們方法生成的描述明顯更受青睞。我們的研究結果表明，這一基於LLM的代理框架在自動化大規模定向營銷方面具有潛力，同時確保僅基於事實的負責任生成。

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.