# Large Language Models for Explainable Decisions in Dynamic Digital Twins

arXiv: https://arxiv.org/abs/2405.14411 · HTML: https://mlsanity.com/paper/2405.14411/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 23 May 2024.

## Builds on (most cited first, 8 of 8)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Explainability for Large Language Models: A Survey](https://mlsanity.com/paper/2309.01029/) (2023)
- [GPT-Based Models Meet Simulation: How to Efficiently Use Large-Scale Pre-Trained Language Models Across Simulation Tasks](https://mlsanity.com/paper/2306.13679/) (2023)
- [An LLM-Based Digital Twin for Optimizing Human-in-the Loop Systems](https://mlsanity.com/paper/2403.16809/) (2024)
- [TwinExplainer: Explaining Predictions of an Automotive Digital Twin](https://mlsanity.com/paper/2302.00152/) (2023)
- [Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins](https://mlsanity.com/paper/2207.09106/) (2022)

## Built on by (most cited first, 4 of 4)

- [Simulation Agent: A Framework for Integrating Simulation and Large Language Models for Enhanced Decision-Making](https://mlsanity.com/paper/2505.13761/) (2025)
- [SimBench: A Framework for Evaluating and Diagnosing LLM-Based Digital-Twin Generation for Multi-Physics Simulation](https://mlsanity.com/paper/2408.11987/) (2024)
- [TwinLoop: Simulation-in-the-Loop Digital Twins for Online Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2604.06610/) (2026)
- [DTScribe: Generating Models for Digital Twin Reporting using Local Large Language Models](https://mlsanity.com/paper/2610.18635/) (2026)

## Abstract (arXiv)

Dynamic data-driven Digital Twins (DDTs) can enable informed decision-making and provide an optimisation platform for the underlying system. By leveraging principles of Dynamic Data-Driven Applications Systems (DDDAS), DDTs can formulate computational modalities for feedback loops, model updates and decision-making, including autonomous ones. However, understanding autonomous decision-making often requires technical and domain-specific knowledge. This paper explores using large language models (LLMs) to provide an explainability platform for DDTs, generating natural language explanations of the system's decision-making by leveraging domain-specific knowledge bases. A case study from smart agriculture is presented.