  Recent studies seek to provide Graph Neural Network (GNN) interpretability
via multiple unsupervised learning models. Due to the scarcity of datasets,
current methods easily suffer from learning bias. To solve this problem, we
embed a Large Language Model (LLM) as knowledge into the GNN explanation
network to avoid the learning bias problem. We inject LLM as a Bayesian
Inference (BI) module to mitigate learning bias. The efficacy of the BI module
has been proven both theoretically and experimentally. We conduct experiments
on both synthetic and real-world datasets. The innovation of our work lies in
two parts: 1. We provide a novel view of the possibility of an LLM functioning
as a Bayesian inference to improve the performance of existing algorithms; 2.
We are the first to discuss the learning bias issues in the GNN explanation
problem.
