Large Language Models for Explainable Decisions in Dynamic Digital Twins

TL;DR

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.

Abstract

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.

Figures (4)

• Figure 1: LLM enabled explainability in a DDT system.
• Figure 2: The DDDAS loop of the DDT in the smart farming application scenario.
• Figure 3: DDDAS/DDT decision-making and LLM's explanation with RAG.
• Figure : The planning workflow