  In this work, we propose a novel method for Bayesian Networks (BNs) structure
elicitation that is based on the initialization of several LLMs with different
experiences, independently querying them to create a structure of the BN, and
further obtaining the final structure by majority voting. We compare the method
with one alternative method on various widely and not widely known BNs of
different sizes and study the scalability of both methods on them. We also
propose an approach to check the contamination of BNs in LLM, which shows that
some widely known BNs are inapplicable for testing the LLM usage for BNs
structure elicitation. We also show that some BNs may be inapplicable for such
experiments because their node names are indistinguishable. The experiments on
the other BNs show that our method performs better than the existing method
with one of the three studied LLMs; however, the performance of both methods
significantly decreases with the increase in BN size.
