  Large Language Models (LLMs) recently demonstrated capabilities for
generating source code in common programming languages. Additionally,
commercial products such as ChatGPT 4 started to provide code interpreters,
allowing for the automatic execution of generated code fragments, instant
feedback, and the possibility to develop and refine in a conversational
fashion. With an exploratory research approach, this paper applies code
generation and interpretation to conceptual models. The concept and prototype
of a conceptual model interpreter is explored, capable of rendering visual
models generated in textual syntax by state-of-the-art LLMs such as Llama~2 and
ChatGPT 4. In particular, these LLMs can generate textual syntax for the
PlantUML and Graphviz modeling software that is automatically rendered within a
conversational user interface. The first result is an architecture describing
the components necessary to interact with interpreters and LLMs through APIs or
locally, providing support for many commercial and open source LLMs and
interpreters. Secondly, experimental results for models generated with ChatGPT
4 and Llama 2 are discussed in two cases covering UML and, on an instance
level, graphs created from custom data. The results indicate the possibility of
modeling iteratively in a conversational fashion.
