aiqbench analysis
This research tackles the specific challenge of applying LLMs to Modelica, a domain-specific language for simulating physical systems where standard code generation often fails. The authors introduce a specialized workflow combining supervised fine-tuning and graph retrieval-augmented generation to significantly improve simulation success rates and code reliability. This work highlights critical advancements in verticalizing general AI models for complex engineering simulations rather than offering broad consumer applications.
ModiGen: A Large Language Model-Based Workflow for Multi-Task Modelica Code Generation
arXiv:2503.18460v2 Announce Type: replace-cross 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.
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.
Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
arXiv:2503.18460 [cs.SE]
(or arXiv:2503.18460v2 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2503.18460 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 258 words
