用大模型把人话变设计代码,让建筑构思更自由
Mediating Modes of Thought: LLM's for design scripting
- 多层大模型代理理解自然语言,生成几何操作序列
- 能完整生成中等复杂度的可视化脚本,复杂度超限则失败
- 适合建筑师和设计师快速实现创意,提升创作乐趣
建筑师使用视觉编程和参数化设计工具探索更广阔的创作空间(Coates, 2010),优化对几何逻辑的理解(Woodbury, 2010),并突破传统软件限制(Burry, 2011)。尽管过去二十年努力使设计编程更易用,但设计师自由思维与算法僵硬性之间仍存在断层(Burry, 2011)。大语言模型(LLMs)的发展或可改变这一局面,因其具备对人类语境的通用理解,并能生成几何逻辑。本研究推测,若LLMs能有效弥合用户意图与算法之间的鸿沟,将极大推动设计脚本的普及与趣味性。我们探索系统是否能通过自然语言提示,自动组合计算设计中的几何操作。系统采用多层LLM代理,赋予特定上下文以推断用户意图、构建逻辑序列,并映射为具体软件命令。用户输入高层级文本后,系统生成几何描述,提炼为操作序列,最终在用户的可视化编程界面中构建完整脚本。系统在一定复杂度内成功生成完整脚本,但超过该阈值时失败。结果表明,LLMs能显著提升设计脚本与人类创造力的契合度。未来应探索对话式交互、多模态输入输出,并评估此类工具的实际性能。
原文摘要 · Abstract (English)
Architects adopt visual scripting and parametric design tools to explore more expansive design spaces (Coates, 2010), refine their thinking about the geometric logic of their design (Woodbury, 2010), and overcome conventional software limitations (Burry, 2011). Despite two decades of effort to make design scripting more accessible, a disconnect between a designer's free ways of thinking and the rigidity of algorithms remains (Burry, 2011). Recent developments in Large Language Models (LLMs) suggest this might soon change, as LLMs encode a general understanding of human context and exhibit the capacity to produce geometric logic. This project speculates that if LLMs can effectively mediate between user intent and algorithms, they become a powerful tool to make scripting in design more widespread and fun. We explore if such systems can interpret natural language prompts to assemble geometric operations relevant to computational design scripting. In the system, multiple layers of LLM agents are configured with specific context to infer the user intent and construct a sequential logic. Given a user's high-level text prompt, a geometric description is created, distilled into a sequence of logic operations, and mapped to software-specific commands. The completed script is constructed in the user's visual programming interface. The system succeeds in generating complete visual scripts up to a certain complexity but fails beyond this complexity threshold. It shows how LLMs can make design scripting much more aligned with human creativity and thought. Future research should explore conversational interactions, expand to multimodal inputs and outputs, and assess the performance of these tools.
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