arXiv cs.AI· K\'erian Fiter, Angelica Portocarrero, Bentley Oakes·· 3 天前AI 评分20
DTScribe：用本地大语言模型生成数字孪生报告模型
DTScribe: Generating Models for Digital Twin Reporting using Local Large Language Models
AI 导读
DTScribe 可读取数字孪生文档，自动输出概念架构可视化与报告页面，并生成 Ontological Modeling Language 描述模型。它通过大语言模型评审与本体推理的细化循环提升产物质量，使约 20B 参数的本地中型模型达到接近更大模型的效果。研究建议避免用大语言模型做评审、避免用聚类做特征提取，并推荐至少两轮推理循环。
正文 · 原文
Abstract:The development of a digital twin brings together many stakeholders and technical components. We have previously investigated the reporting of the conceptual architecture for these digital twins as an artifact for discussion. However, we find that the activity of modeling the digital twin for visualization and consensus-building can be quite tedious and time-consuming.
In this article, we explore the automation of model generation using large language models, targeting the model required for digital twin reporting. Our approach is designed to ingest digital twin documents and output both the conceptual architecture visualization and a reporting page for that digital twin. We first extract from the input the key details aligning with our twenty-one-characteristic Digital Twin Description Framework described in previous work. Then, we generate a description model in the Ontological Modeling Language, representing the digital twin as a constellation of models, data, enablers, and services. We utilize refinement loops with large language models acting as judges and ontological reasoning to increase artifact quality as measured by our metrics.
We investigate how architectural and parameter choices affect artifact quality, such as how refinement loops enable local large language models to produce sufficiently high-quality results comparable to larger models. We also detail insights and lessons learned useful for practitioners, such that they can integrate our techniques to accelerate their digital twin reporting or for other model generation tasks. These insights include the use of medium-sized local models (around 20 billion parameters), the avoidance of large language models as judges and clustering for characteristic extraction, and the recommendation of at least two reasoner loops for Ontological Modeling Language refinement.
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.18635 [cs.SE] |
| (or arXiv:2610.18635v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2610.18635 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Kérian Fiter [view email]
[v1]
Tue, 13 Oct 2026 16:07:43 UTC (2,834 KB)
来源：arXiv cs.AI · arxiv.org