Recently, large pretrained language models have achieved compelling
performance on commonsense benchmarks. Nevertheless, it is unclear what
commonsense knowledge the models learn and whether they solely exploit spurious
patterns. Feature attributions are popular explainability techniques that
identify important input concepts for model outputs. However, commonsense
knowledge tends to be implicit and rarely explicitly presented in inputs. These
methods cannot infer models' implicit reasoning over mentioned concepts. We
present CommonsenseVIS, a visual explanatory system that utilizes external
commonsense knowledge bases to contextualize model behavior for commonsense
question-answering. Specifically, we extract relevant commonsense knowledge in
inputs as references to align model behavior with human knowledge. Our system
features multi-level visualization and interactive model probing and editing
for different concepts and their underlying relations. Through a user study, we
show that CommonsenseVIS helps NLP experts conduct a systematic and scalable
visual analysis of models' relational reasoning over concepts in different
situations.

利用外部常识知识库将模型行为与人类知识对齐，并通过多层次可视化和交互式模型探索和编辑，帮助自然语言处理专家在不同场景中对模型的概念关系推理进行系统且可扩展的可视化分析。