From over-reliance to smart integration: using Large-Language Models as translators between specialized modeling and simulation tools

• URL: http://arxiv.org/abs/2506.11141v1
• Date: Wed, 11 Jun 2025 02:39:08 GMT
• Title: From over-reliance to smart integration: using Large-Language Models as translators between specialized modeling and simulation tools
• Authors: Philippe J. Giabbanelli, John Beverley, Istvan David, Andreas Tolk,
• Abstract summary: Large Language Models (LLMs) offer transformative potential for Modeling & Simulation (M&S)<n>Over-reliance risks compromising quality due to ambiguities, logical shortcuts, and hallucinations.<n>This paper advocates integrating LLMs as translators between specialized tools to mitigate complexity in M&S tasks.
• Score: 0.3749861135832073
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large Language Models (LLMs) offer transformative potential for Modeling & Simulation (M&S) through natural language interfaces that simplify workflows. However, over-reliance risks compromising quality due to ambiguities, logical shortcuts, and hallucinations. This paper advocates integrating LLMs as middleware or translators between specialized tools to mitigate complexity in M&S tasks. Acting as translators, LLMs can enhance interoperability across multi-formalism, multi-semantics, and multi-paradigm systems. We address two key challenges: identifying appropriate languages and tools for modeling and simulation tasks, and developing efficient software architectures that integrate LLMs without performance bottlenecks. To this end, the paper explores LLM-mediated workflows, emphasizes structured tool integration, and recommends Low-Rank Adaptation-based architectures for efficient task-specific adaptations. This approach ensures LLMs complement rather than replace specialized tools, fostering high-quality, reliable M&S processes.

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