Towards Faithful Knowledge Graph Explanation Through Deep Alignment in Commonsense Question Answering (2024)

Abstract: 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 (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.

## introduction in commonsense reasoning problems, many rely on both explicit textual information and structured domain knowledge @xcite to compensate for the limited factual memory of lms @xcite and provide insights into the inference processes @xcite , however explanations can also be expressed by highlighting a subset of this knowledge. making the model output the facts used to answer a particular question can increase trustworthiness and help with debugging. effective explanations should accurately reflect the reasoning process of a model @xcite . in knowledge-augmented commonsense qa, attention weights from messagepassing have been used to provide poc-hoc explanations @xcite , : corresponding author. ## lm-kg explanation evaluation metrics here we evalute gnn explanability in a fusion model in two folds, namely, faithfulness and sparsity. with "faithful graph encoders", we refer to gnn representations being able to reflect the genuine rationale of the prediction. while sparsity means rationales should capture the most important input features and ignore the irrelevant ones. we argue that lm-kg fusion models are intrinsically unable to provide graph-structured explanations that are highly faithful to the full model. ## methodology to achieve a more faithful lm-kg interpretation, it's imperative to ensure that the introduced modifications of models do not substantially deviate from the lm's behaviour, implying that after introducing modifications, the gnn encoder should predict a target distribution that mirrors the one emitted by the unaltered model to retain its subtle reasoning ability. while traditional methods have relied heavily on cross-entropy as the primary objective, the unfaithful gnn encoder of existing lm-kg models demands a more nuanced regularisation of training procedure. we next introduce lm-kg distribution-aware alignment (lkda) to bridge this gap. ## results analysis & discussion ## limitations while lkda enhances explanation faithfulness in lm-kg models, some limitations exist. evaluation relies on perturbation methods due to lack of ground-truth explanations, which may not fully capture explanation. lkda introduces computational overhead, potentially restricting applicability to larger models and datasets. lkda assumes a specific lm-kg architecture, and adapting it to other architectures may require further modifications. quantitative metrics should be complemented with human evaluations to assess plausibility and understandability. future research should incorporate user studies. algorithm 1 lkda training and explanation process require: text @xmath0; a], background subgraph g, model m 1: input: question q, answer @xmath1, subgraph g 2: initialize: language model encoder lm, graph encoder e kg 3: step 1: text and graph encoding 4: text representations: z lm ← lm(s) 5: generate graph embeddings: e kg ← f g (g) 6: step 2: fusion and masking 7: combine z lm and e kg in the fusion module: f(z, g) 8: mask text representation: m \z 9: calculate target distribution p (y |m \z ) 10: step 3: alignment and optimization 11: minimize jensen-shannon divergence: 12: j(p (y |m), p (y |m \z )) 13: update model parameters θ t ← θ t -∇ θt l 14: step 4: post-hoc explanations 15: analyze attention weights α ij to identify key weighted edges in g 16: output: faithful explanations indicating reasoning process of model m