43rd International Conference on Conceptual Modeling (ER 2024)

The advent of generative artificial intelligence, and in particular large language models, has opened up new possibilities for information processing in a multitude of domains. Nevertheless, it is essential to validate their output in order to ensure its validity within the specified context. This is due to their nature as probabilistic models of language, which may lead to the generation of inaccuracies or non-existent facts commonly known as hallucinations. As a solution, we propose a framework and a prompt structure for the validation of the results of generative artificial intelligence in formats that are more human-comprehensible through the use of conceptual models. We denote this as conceptual model augmented generative artificial intelligence (CMAG). We illustrate the approach through application examples in the domains of data management, knowledge graphs and cultural heritage, and software engineering.