大模型
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ModiGen：一种基于大语言模型的多任务 Modelica 代码生成工作流
该论文针对 Modelica 物理系统建模中 LLM 代码生成能力不足的问题，构建了评估 LLM 生成 Modelica 组件模型与测试用例的基准数据集，发现现有模型生成的代码常无法成功仿真。作者提出整合监督微调、图检索增强生成与反馈优化的工作流，报告 pass@1 在组件生成任务上最大提升 0.3349、测试用例生成任务上提升 0.2457。论文未在摘要中披露基线模型、数据集规模及绝对通过率，效果仍需进一步验证。
AI推荐理由 arXiv 论文（v2，2026-09-13 修订），来源可信、摘要信息密度高。核心新信息：作者构建了评估 LLM 生成 Modelica 组件模型与测试用例的基准数据集，发现现有 LLM 生成的代码常无法成功仿真；并提出结合监督微调、图检索增强生成（Graph RAG）与反馈优化的工作流，报告 pass@1 在组件生成任务上最大提升 0.3349、测试用例生成任务提升 0.2457。局限：摘要未给出基线模型、数据集规模与绝对通过率，提升幅度为相对/绝对口径不明，且未见第三方复现，实际工程可用性待验证。对 LLM 代码生成、科学仿真与工程建模方向有参考价值。
72
正文
Computer Science > Software Engineering
arXiv:2503.18460 (cs)
[Submitted on 24 Mar 2025 (v1), last revised 13 Sep 2026 (this version, v2)]
Title:ModiGen: A Large Language Model-Based Workflow for Multi-Task Modelica Code Generation
Authors:Jiahui Xiang, Tong Ye, Peiyu Liu, Yinan Zhang, Wenhai Wang
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Abstract:Modelica is a widely adopted language for simulating complex physical systems, yet effective model creation and optimization require substantial domain expertise. Although large language models (LLMs) have demonstrated promising capabilities in code generation, their application to modeling remains largely unexplored. To address this gap, we have developed benchmark datasets specifically designed to evaluate the performance of LLMs in generating Modelica component models and test cases. Our evaluation reveals substantial limitations in current LLMs, as the generated code often fails to simulate successfully. To overcome these challenges, we propose a specialized workflow that integrates supervised fine-tuning, graph retrieval-augmented generation, and feedback optimization to improve the accuracy and reliability of Modelica code generation. The evaluation results demonstrate significant performance gains: the maximum improvement in pass@1 reached 0.3349 for the component generation task and 0.2457 for the test case generation task. This research underscores the potential of LLMs to advance intelligent modeling tools and offers valuable insights for future developments in system modeling and engineering applications.
Subjects:
Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
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arXiv:2503.18460 [cs.SE]
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arXiv:2503.18460v2 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2503.18460
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Submission history
From: Jiahui Xiang [view email]
[v1]
Mon, 24 Mar 2025 09:04:49 UTC (2,994 KB)
[v2]
Sun, 13 Sep 2026 04:34:40 UTC (1,026 KB)
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