mlsanityData to 14 Oct 2026

Large Language Models for Explainable Decisions in Dynamic Digital Twins

arXiv 2405.14411 ↗First posted 23 May 2024cs.AI cs.SY eess.SYcited by 4

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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.

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

8 of 23 references.

References · 8

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
  4. 2023Explainability for Large Language Models: A Surveycited by 333
  5. 2023GPT-Based Models Meet Simulation: How to Efficiently Use Large-Scale Pre-Trained Language Models Across Simulation Taskscited by 14
  6. 2024An LLM-Based Digital Twin for Optimizing Human-in-the Loop Systemscited by 13
  7. 2023TwinExplainer: Explaining Predictions of an Automotive Digital Twincited by 6
  8. 2022Explainable Human-in-the-loop Dynamic Data-Driven Digital Twinscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 2.

4 papers.

  1. 2025Simulation Agent: A Framework for Integrating Simulation and Large Language Models for Enhanced Decision-Makingcited by 4
  2. 2024SimBench: A Framework for Evaluating and Diagnosing LLM-Based Digital-Twin Generation for Multi-Physics Simulationcited by 3
  3. 2026TwinLoop: Simulation-in-the-Loop Digital Twins for Online Multi-Agent Reinforcement Learningcited by 0
  4. 2026DTScribe: Generating Models for Digital Twin Reporting using Local Large Language Modelscited by 0