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:建筑设计是一个非常复杂的实践,涉及各种各样的学科、技术、专有设计软件、专门知识和几乎无限的限制,涉及各种各样的设计任务。 使直观、可获取和可扩展的设计过程成为面向所有人实现性能驱动和可持续设计的重要一步。 为此,我们引入了Archtext, 这是一种新型的语义生成辅助工具。 考古文本使得设计能够仅使用自然语言提示进行生成,而以大规模语言模型作为投入。 我们对考古文本的下游任务性能进行了彻底的定量评估,重点是从1.2亿至60亿参数的预培训语言模型的语义准确性和多样性。 考古文本模型能够学习具体的设计任务,以近100<unk> 的速度生成有效的住宅布局。 准确性在扩大模型规模时显示出巨大的改进,最大的模型(GPT-J)为不同快速类别提供了惊人的精确度,从25%到80%以上不等的精确度。 我们公开来源了精细的考古模型和我们的合成数据组,希望激发这一令人兴奋的设计领域的实验。</s>