  Model slicing is a useful technique for identifying a subset of a larger
model that is relevant to fulfilling a given requirement. Notable applications
of slicing include reducing inspection effort when checking design adequacy to
meet requirements of interest and when conducting change impact analysis. In
this paper, we present a method based on large language models (LLMs) for
extracting model slices from graphical Simulink models. Our approach converts a
Simulink model into a textual representation, uses an LLM to identify the
necessary Simulink blocks for satisfying a specific requirement, and constructs
a sound model slice that incorporates the blocks identified by the LLM. We
explore how different levels of granularity (verbosity) in transforming
Simulink models into textual representations, as well as the strategy used to
prompt the LLM, impact the accuracy of the generated slices. Our preliminary
findings suggest that prompts created by textual representations that retain
the syntax and semantics of Simulink blocks while omitting visual rendering
information of Simulink models yield the most accurate slices. Furthermore, the
chain-of-thought and zero-shot prompting strategies result in the largest
number of accurate model slices produced by our approach.
