A systematic review of 18 studies finds LLMs are increasingly used for software architecture tasks, mostly via zero-shot prompting, with one-third of studies lacking baseline comparisons.
Large Language Models for Explainable Decisions in Dynamic Digital Twins
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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.
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Software Architecture Meets LLMs: A Systematic Literature Review
A systematic review of 18 studies finds LLMs are increasingly used for software architecture tasks, mostly via zero-shot prompting, with one-third of studies lacking baseline comparisons.