AI Realtor: Towards Grounded Persuasive Language Generation for Automated Copywriting
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909008613343232 |
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| author | Wu, Jibang Yang, Chenghao Wu, Yi Mahns, Simon Wang, Chaoqi Zhu, Hao Fang, Fei Xu, Haifeng |
| author_facet | Wu, Jibang Yang, Chenghao Wu, Yi Mahns, Simon Wang, Chaoqi Zhu, Hao Fang, Fei Xu, Haifeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16810 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI Realtor: Towards Grounded Persuasive Language Generation for Automated Copywriting Wu, Jibang Yang, Chenghao Wu, Yi Mahns, Simon Wang, Chaoqi Zhu, Hao Fang, Fei Xu, Haifeng Artificial Intelligence Computation and Language Human-Computer Interaction General Economics Economics 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. |
| title | AI Realtor: Towards Grounded Persuasive Language Generation for Automated Copywriting |
| topic | Artificial Intelligence Computation and Language Human-Computer Interaction General Economics Economics |
| url | https://arxiv.org/abs/2502.16810 |