  The fusion of language models (LMs) and knowledge graphs (KGs) is widely used
in commonsense question answering, but generating faithful explanations remains
challenging. Current methods often overlook path decoding faithfulness, leading
to divergence between graph encoder outputs and model predictions. We identify
confounding effects and LM-KG misalignment as key factors causing spurious
explanations. To address this, we introduce the LM-KG Fidelity metric to assess
KG representation reliability and propose the LM-KG Distribution-aware
Alignment (\textit{LKDA}) algorithm to improve explanation faithfulness.
Without ground truth, we evaluate KG explanations using the proposed
Fidelity-Sparsity Trade-off Curve. Experiments on CommonsenseQA and OpenBookQA
show that LKDA significantly enhances explanation fidelity and model
performance, highlighting the need to address distributional misalignment for
reliable commonsense reasoning.
