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）预测模型有望增强对给定约束的符合性、实现系统性泛化以及可解释性，因为它们能够通过推理从亚符号输入中提取的高层概念来推断与某些先验知识一致的标签。近期研究表明，NeSy预测器会受到推理捷径的影响：它们可以通过利用具有非预期语义的概念达到高精度，但因此无法实现其承诺的优势。然而，对推理捷径的系统性表征及潜在缓解策略仍属空白。本研究通过将推理捷径刻画为学习目标函数的非预期最优解，并识别其出现的四个关键条件来填补这一空白。基于此，我们推导出若干自然的缓解策略，并从理论和实证两方面分析其有效性。我们的分析表明，推理捷径难以应对，这对现有NeSy解决方案的可信度与可解释性提出了质疑。