用大模型自动构建汽车领域元模型，支持可视化与人工迭代优化。
LLM-based Iterative Approach to Metamodeling in Automotive
- 基于大模型解析汽车需求，生成Ecore元模型
- 通过PlantUML可视化元模型，支持专家反馈修正
- 提供可本地部署方案，适合汽车系统建模场景
本文提出一种基于大语言模型（LLM）的自动化领域特定元模型构建方法，聚焦于汽车领域应用。实现了一个基于Python的Web服务原型，采用OpenAI的GPT-4o作为底层大模型。初步实验表明，该方法能根据一组汽车需求成功构建Ecore元模型，并利用PlantUML语法进行可视化，便于人类专家提供反馈以进一步优化结果。同时探讨了本地部署的可行性，分析了相关限制及所需额外步骤。
原文摘要 · Abstract (English)
In this paper, we introduce an automated approach to domain-specific metamodel construction relying on Large Language Model (LLM). The main focus is adoption in automotive domain. As outcome, a prototype was implemented as web service using Python programming language, while OpenAI's GPT-4o was used as the underlying LLM. Based on the initial experiments, this approach successfully constructs Ecore metamodel based on set of automotive requirements and visualizes it making use of PlantUML notation, so human experts can provide feedback in order to refine the result. Finally, locally deployable solution is also considered, including the limitations and additional steps required.
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