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.

基于多个经验初始化的 LLMs，并通过独立查询它们创建 BN 的结构，并通过多数投票获取最终结构的贝叶斯网络（BNs）结构获取方法在不同规模 BN 上与另一种替代方法进行比较，研究了两种方法在可广泛和不广泛知名的 BNs 上的可扩展性。

大型语言模型中贝叶斯网络结构获取的可扩展性：一种新方法和比较分析