用检索增强生成技术提升汽车设计中的智能辅助能力
Adopting RAG for LLM-Aided Future Vehicle Design
- 结合RAG与大模型，实现上下文感知的汽车设计问答
- LLAMA3和Mistral在本地部署下准确率表现良好，延迟可控
- 适合关注数据隐私的车企研发团队使用
本文探索将大语言模型（LLMs）与检索增强生成（RAG）结合，用于提升汽车行业的自动化设计与软件开发效率。通过两个案例研究：标准化合规聊天机器人和设计协同助手，均采用RAG技术实现精准、上下文相关的响应。对比评估了GPT-4o、LLAMA3、Mistral和Mixtral四款模型的答对准确率与执行时间。结果表明，尽管GPT-4性能更优，但LLAMA3与Mistral在本地部署中也展现出良好潜力，有助于解决汽车行业中的数据隐私问题。本研究展示了RAG增强型大模型在汽车工程设计流程与合规性支持中的应用前景。
原文摘要 · Abstract (English)
In this paper, we explore the integration of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to enhance automated design and software development in the automotive industry. We present two case studies: a standardization compliance chatbot and a design copilot, both utilizing RAG to provide accurate, context-aware responses. We evaluate four LLMs-GPT-4o, LLAMA3, Mistral, and Mixtral -- comparing their answering accuracy and execution time. Our results demonstrate that while GPT-4 offers superior performance, LLAMA3 and Mistral also show promising capabilities for local deployment, addressing data privacy concerns in automotive applications. This study highlights the potential of RAG-augmented LLMs in improving design workflows and compliance in automotive engineering.
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