Neuro-Symbolic (NeSy) predictive models hold the promise of improved compliance with given constraints, systematic generalization, and interpretability, as they allow to infer labels that are consistent with some prior knowledge by reasoning over high-level concepts extracted from sub-symbolic inputs. It was recently shown that NeSy predictors are affected by reasoning shortcuts: they can attain high accuracy but by leveraging concepts with unintended semantics, thus coming short of their promised advantages. Yet, a systematic characterization of reasoning shortcuts and of potential mitigation strategies is missing. This work fills this gap by characterizing them as unintended optima of the learning objective and identifying four key conditions behind their occurrence. Based on this, we derive several natural mitigation strategies, and analyze their efficacy both theoretically and empirically. Our analysis shows reasoning shortcuts are difficult to deal with, casting doubts on the trustworthiness and interpretability of existing NeSy solutions.

翻译：神经符号（NeSy）预测模型有望提升对给定约束的遵循能力、系统泛化能力和可解释性，因为它们能够通过推理从亚符号输入中提取的高层概念，推断出与某些先验知识一致的标签。然而，近期研究表明NeSy预测器会受到推理捷径的影响：它们可能通过利用具有非预期语义的概念来获得高准确率，从而未能实现其承诺的优势。尽管如此，目前仍缺乏对推理捷径的系统性特征刻画及潜在的缓解策略研究。本文通过将推理捷径表征为学习目标的非预期最优解，并识别其发生的四个关键条件，填补了这一空白。基于此，我们推导出多种自然缓解策略，并从理论和实证两个层面分析其有效性。我们的分析表明，推理捷径难以处理，这使人们质疑现有NeSy解决方案的可信度与可解释性。