Ruizhe Shao


2025

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ArchiDocGen: Multi-Agent Framework for Expository Document Generation in the Architectural Industry
Junjie Jiang | Haodong Wu | Yongqi Zhang | Songyue Guo | Bingcen Liu | Caleb Chen Cao | Ruizhe Shao | Chao Guan | Peng Xu | Lei Chen
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)

The architectural industry produces extensive documents, including method statements—expository documents that integrate multi-source data into actionable guidance. Manual drafting however is labor-intensive and time-consuming. This paper introduces ArchiDocGen, a multi-agent framework automating method statement generation. Unlike traditional approaches relying on static templates or single-pass generation, ArchiDocGen decomposes the task into three steps: outline generation, section-based content generation, and polishing, each handled by specialized agents. To provide domain expertise, ArchiDocGen employs a section-based retriever to fetch and synthesize relevant documents from its custom knowledge base. Each section is generated through iterative reasoning of a section-based chain-of-thought (SeCoT) scheme, followed by refinement to meet professional standards. To evaluate the generated method statements, we partner with the industry to establish a multi-dimensional evaluation system by combining automatic and empirical methods. Experiments show that ArchiDocGen achieves 4.38 ContentScore, excelling in specialization, completeness, organization, and clarity. Additionally, a web-based application for ArchiDocGen is developed and deployed with industry partners.