用检索增强生成提升大模型对闭源仿真软件的理解能力

Large Language Models (LLMs) are tools that have become indispensable in development and programming. However, they suffer from hallucinations, especially when dealing with unknown knowledge. This is particularly the case when LLMs are to be used to support closed-source software applications. Retrieval-Augmented Generation (RAG) offers an approach to use additional knowledge alongside the pre-trained knowledge of the LLM to respond to user prompts. Possible tasks range from a smart-autocomplete, text extraction for question answering, model summarization, component explaining, compositional reasoning, to creation of simulation components and complete input models. This work tests existing RAG systems for closed-source simulation frameworks, in our case the mesh-free simulation software Pasimodo. Since data protection and intellectual property rights are particularly important for problems solved with closed-source software, the tests focus on execution using local LLMs. In order to enable smaller institutions to use the systems, smaller language models will be tested first. The systems show impressive results, but often fail due to insufficient information. Different approaches for improving response quality are tested. In particular, tailoring the information provided to the LLMs dependent to the prompts proves to be a significant improvement. This demonstrates the great potential and the further work needed to improve information retrieval for closed-source simulation models.