Huge Language Fashions for Explainable Selections in Dynamic Digital Twins
Authors: Nan Zhang, Christian Vergara-Marcillo, Georgios Diamantopoulos, Jingran Shen, Nikos Tziritas, Rami Bahsoon, Georgios Theodoropoulos
Abstract: Dynamic data-driven Digital Twins (DDTs) can permit educated decision-making and provide an optimisation platform for the underlying system. By leveraging concepts of Dynamic Data-Pushed Functions Strategies (DDDAS), DDTs can formulate computational modalities for solutions loops, model updates and decision-making, along with autonomous ones. However, understanding autonomous decision-making sometimes requires technical and domain-specific info. This paper explores using large language fashions (LLMs) to supply an explainability platform for DDTs, producing pure language explanations of the system’s decision-making by leveraging domain-specific info bases. A case analysis from smart agriculture is launched.
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