Neuro-Symbolic (NeSy) predictors that conform to symbolic knowledge - encoding, e.g., safety constraints - can be affected by Reasoning Shortcuts (RSs): They learn concepts consistent with the symbolic knowledge by exploiting unintended semantics. RSs compromise reliability and generalization and, as we show in this paper, they are linked to NeSy models being overconfident about the predicted concepts. Unfortunately, the only trustworthy mitigation strategy requires collecting costly dense supervision over the concepts. Rather than attempting to avoid RSs altogether, we propose to ensure NeSy models are aware of the semantic ambiguity of the concepts they learn, thus enabling their users to identify and distrust low-quality concepts. Starting from three simple desiderata, we derive bears (BE Aware of Reasoning Shortcuts), an ensembling technique that calibrates the model's concept-level confidence without compromising prediction accuracy, thus encouraging NeSy architectures to be uncertain about concepts affected by RSs. We show empirically that bears improves RS-awareness of several state-of-the-art NeSy models, and also facilitates acquiring informative dense annotations for mitigation purposes.

翻译：神经符号（NeSy）预测器遵循符号知识（例如编码安全约束）时，可能受到推理捷径（RSs）的影响：它们通过利用非预期语义学习与符号知识一致的概念。推理捷径会损害可靠性和泛化能力，且正如本文所示，它们与NeSy模型对预测概念过度自信有关。遗憾的是，唯一可信的缓解策略需要收集昂贵的密集概念监督。我们不试图完全避免推理捷径，而是提出确保NeSy模型意识到所学习概念的语义模糊性，从而使用户能够识别并怀疑低质量概念。基于三个简单目标，我们推导出BEARS（BE Aware of Reasoning Shortcuts），一种集成技术，在不影响预测准确性的情况下校准模型的概念级置信度，从而鼓励NeSy架构对受推理捷径影响的概念保持不确定性。实验表明，BEARS提升了多个最先进NeSy模型的推理捷径意识，并有助于为缓解目的获取信息丰富的密集标注。