Architext：语言驱动的生成式建筑设计
Architext: Language-Driven Generative Architecture Design
AI总结：
本研究提出新型语义生成辅助工具Architext，支持仅通过自然语言提示词调用大语言模型生成建筑设计，经多规模模型定量验证生成准确率优异，并开源相关模型与合成数据集。
摘要：
建筑设计是一项高度复杂的实践，涵盖众多学科、技术、专有设计软件、专业知识，以及在各类设计任务中几乎无穷无尽的约束条件。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可持续设计的重要一步。为此，我们推出了Architext——一款新型语义生成辅助工具。Architext仅需将输入给大语言模型的自然语言提示词作为输入，即可实现设计生成。我们对Architext的下游任务性能开展了全面的定量评估，重点关注参数规模从1.2亿到60亿不等的多款预训练语言模型的语义准确性与多样性。Architext模型能够学习特定的设计任务，生成有效住宅布局的成功率接近100%。模型规模扩大时，准确性显著提升，其中最大的模型（GPT-J）在不同提示词类别下的准确率表现亮眼，区间为25%到80%以上。我们开源了经过微调的Architext模型以及我们的合成数据集，以期激发这一充满活力的设计研究领域的探索。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
