评测大模型在土木工程绘图修订中的自动化能力，填补工业场景评估空白。
DrafterBench: Benchmarking Large Language Models for Tasks Automation in Civil Engineering
- 构建12类真实绘图任务，含46个自定义工具与1920个具体任务
- 覆盖长文本理解、知识调用、动态指令适应等关键能力测试
- 开源数据集助力工程师优化大模型在工程场景的落地表现
大语言模型代理在解决现实问题方面展现出巨大潜力，有望成为工业自动化解决方案。然而，针对工业场景（如土木工程）系统性评估自动化代理的基准仍显不足。为此，我们提出DrafterBench，用于全面评估大模型代理在技术绘图修订这一土木工程典型任务中的表现。DrafterBench基于真实绘图文件归纳出12类任务，包含46个自定义函数/工具，共计1920个任务。该基准开源，旨在严格测试AI代理在理解复杂长上下文指令、利用先验知识以及通过隐式策略感知适应动态指令质量方面的表现。工具包全面评估结构化数据理解、函数执行、指令遵循与批判性推理等能力。提供详细的任务准确率与错误统计分析，以深入洞察代理能力并识别大模型在工程应用中需改进的方向。基准代码与测试集已公开于https://github.com/Eason-Li-AIS/DrafterBench及https://huggingface.co/datasets/Eason666/DrafterBench。
原文摘要 · Abstract (English)
Large Language Model (LLM) agents have shown great potential for solving real-world problems and promise to be a solution for tasks automation in industry. However, more benchmarks are needed to systematically evaluate automation agents from an industrial perspective, for example, in Civil Engineering. Therefore, we propose DrafterBench for the comprehensive evaluation of LLM agents in the context of technical drawing revision, a representation task in civil engineering. DrafterBench contains twelve types of tasks summarized from real-world drawing files, with 46 customized functions/tools and 1920 tasks in total. DrafterBench is an open-source benchmark to rigorously test AI agents' proficiency in interpreting intricate and long-context instructions, leveraging prior knowledge, and adapting to dynamic instruction quality via implicit policy awareness. The toolkit comprehensively assesses distinct capabilities in structured data comprehension, function execution, instruction following, and critical reasoning. DrafterBench offers detailed analysis of task accuracy and error statistics, aiming to provide deeper insight into agent capabilities and identify improvement targets for integrating LLMs in engineering applications. Our benchmark is available at https://github.com/Eason-Li-AIS/DrafterBench, with the test set hosted at https://huggingface.co/datasets/Eason666/DrafterBench.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。