Large Language Models for Explainable Decisions in Dynamic Digital Twins
This work addresses the need for explainability in autonomous systems for domain experts, though it appears incremental as it applies existing LLMs to a new application area.
The paper tackles the problem of understanding autonomous decision-making in dynamic digital twins by using large language models to generate natural language explanations, with a case study in smart agriculture demonstrating the approach.
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.