Neuro-symbolic predictors learn a mapping from sub-symbolic inputs to
higher-level concepts and then carry out (probabilistic) logical inference on
this intermediate representation. This setup offers clear advantages in terms
of consistency to symbolic prior knowledge, and is often believed to provide
interpretability benefits in that - by virtue of complying with the knowledge -
the learned concepts can be better understood by human stakeholders. However,
it was recently shown that this setup is affected by reasoning shortcuts
whereby predictions attain high accuracy by leveraging concepts with unintended
semantics, yielding poor out-of-distribution performance and compromising
interpretability. In this short paper, we establish a formal link between
reasoning shortcuts and the optima of the loss function, and identify
situations in which reasoning shortcuts can arise. Based on this, we discuss
limitations of natural mitigation strategies such as reconstruction and concept
supervision.

通过建立神经符号预测器 (即从子符号输入到更高层概念的映射) 提出了一种深度学习的 setup，然后在这种中间表示上进行 (概率) 逻辑推理。本文建立了推理捷径与损失函数的 optima 之间的形式链接，并确定了推理捷径可能出现的情况。在此基础上，讨论了诸如重构和概念监督等自然缓解策略的局限性。