# DTScribe: Generating Models for Digital Twin Reporting using Local Large Language Models

arXiv: https://arxiv.org/abs/2610.18635 · HTML: https://mlsanity.com/paper/2610.18635/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 41 of its 104 references that are in the map.
- First posted on arXiv 13 Oct 2026.

## Builds on (most cited first, 30 of 41)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [Graph of Thoughts: Solving Elaborate Problems with Large Language Models](https://mlsanity.com/paper/2308.09687/) (2023)
- [MTEB: Massive Text Embedding Benchmark](https://mlsanity.com/paper/2210.07316/) (2022)
- [Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking](https://mlsanity.com/paper/2403.09629/) (2024)
- [The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence](https://mlsanity.com/paper/2002.06177/) (2020)
- [Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences](https://mlsanity.com/paper/2404.12272/) (2024)
- [LLMs4OL: Large Language Models for Ontology Learning](https://mlsanity.com/paper/2307.16648/) (2023)
- [Ontology Generation using Large Language Models](https://mlsanity.com/paper/2503.05388/) (2025)
- [Guidelines for Empirical Studies in Software Engineering involving Large Language Models](https://mlsanity.com/paper/2508.15503/) (2025)
- [On the Effectiveness of LLM-as-a-judge for Code Generation and Summarization](https://mlsanity.com/paper/2507.16587/) (2025)
- [OntoChat: a Framework for Conversational Ontology Engineering using Language Models](https://mlsanity.com/paper/2403.05921/) (2024)
- [Do LLMs Really Adapt to Domains? An Ontology Learning Perspective](https://mlsanity.com/paper/2407.19998/) (2024)
- [Generative AI for Requirements Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2409.06741/) (2024)
- [Accelerating Knowledge Graph and Ontology Engineering with Large Language Models](https://mlsanity.com/paper/2411.09601/) (2024)
- [Ontologies in Digital Twins: A Systematic Literature Review](https://mlsanity.com/paper/2308.15168/) (2023)
- [An Evaluation of Requirements Modeling for Cyber-Physical Systems via LLMs](https://mlsanity.com/paper/2408.02450/) (2024)
- [A Short Review for Ontology Learning: Stride to Large Language Models Trend](https://mlsanity.com/paper/2404.14991/) (2024)
- [Ontology-Constrained Generation of Domain-Specific Clinical Summaries](https://mlsanity.com/paper/2411.15666/) (2024)
- [LLM-enabled Instance Model Generation](https://mlsanity.com/paper/2503.22587/) (2025)
- [Generating Software Architecture Description from Source Code using Reverse Engineering and Large Language Model](https://mlsanity.com/paper/2511.05165/) (2025)
- [Large Language Models for Explainable Decisions in Dynamic Digital Twins](https://mlsanity.com/paper/2405.14411/) (2024)
- [A Survey of Document-Level Information Extraction](https://mlsanity.com/paper/2309.13249/) (2023)
- [Aspect-Based Summarization with Self-Aspect Retrieval Enhanced Generation](https://mlsanity.com/paper/2504.13054/) (2025)
- [From over-reliance to smart integration: using Large-Language Models as translators between specialized modeling and simulation tools](https://mlsanity.com/paper/2506.11141/) (2025)
- [Accurate and Consistent Graph Model Generation from Text with Large Language Models](https://mlsanity.com/paper/2508.00255/) (2025)
- [Engineering Automotive Digital Twins on Standardized Architectures: A Case Study](https://mlsanity.com/paper/2508.18662/) (2025)
- [A Guide to Large Language Models in Modeling and Simulation: From Core Techniques to Critical Challenges](https://mlsanity.com/paper/2602.05883/) (2026)
- [Automation in Model-Driven Engineering: A look back, and ahead](https://mlsanity.com/paper/2405.18539/) (2024)

## Abstract (arXiv)

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.