Architectural design is a highly complex practice that involves a wide diversity of disciplines, technologies, proprietary design software, expertise, and an almost infinite number of constraints, across a vast array of design tasks. Enabling intuitive, accessible, and scalable design processes is an important step towards performance-driven and sustainable design for all. To that end, we introduce Architext, a novel semantic generation assistive tool. Architext enables design generation with only natural language prompts, given to large-scale Language Models, as input. We conduct a thorough quantitative evaluation of Architext's downstream task performance, focusing on semantic accuracy and diversity for a number of pre-trained language models ranging from 120 million to 6 billion parameters. Architext models are able to learn the specific design task, generating valid residential layouts at a near 100\% rate. Accuracy shows great improvement when scaling the models, with the largest model (GPT-J) yielding impressive accuracy ranging between 25% to over 80% for different prompt categories. We open source the finetuned Architext models and our synthetic dataset, hoping to inspire experimentation in this exciting area of design research.

翻译：建筑设计是一项高度复杂的实践，涉及众多学科、技术、专有设计软件、专业知识以及几乎无限的设计任务约束。实现直观、可及且可扩展的设计流程，是迈向以性能为导向、可持续全民设计的重要一步。为此，我们提出了Architext，一种新型的语义生成辅助工具。Architext仅需利用自然语言提示（输入至大规模语言模型）即可生成设计方案。我们针对Architext的下游任务性能进行了全面的定量评估，重点考察了从1.2亿到60亿参数量的多个预训练语言模型在语义准确性和多样性方面的表现。Architext模型能够学习特定设计任务，以接近100%的成功率生成有效的住宅布局。随着模型规模的扩大，准确性显著提升，其中最大的模型（GPT-J）在不同提示类别上展现出令人瞩目的准确性，范围从25%到超过80%。我们开源了微调后的Architext模型及合成数据集，期望能激发这一令人振奋的设计研究领域的探索。