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，涉及多学科、多种技术、专有设计软件、专业经验以及几乎无穷无尽的约束条件，涵盖广泛的设计任务。实现直观、易用且可扩展的设计流程，是迈向面向所有人的性能驱动型与可持续设计的重要一步。为此，我们提出了Architext，一种新颖的语义生成辅助工具。Architext仅需自然语言提示作为输入，通过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60亿参数规模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，并以接近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）在不同提示类别上的准确率从25%到超过80%不等。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兴奋的设计研究领域的更多探索。