Abstract
Neurosymbolic (NeSy) systems integrate neural networks with logical reasoning to achieve both generalization and interpretability. However, recent work has shown they are susceptible to shortcut reasoning behaviors. We propose a novel method using matrix-based differentiable logic programming to mitigate reasoning shortcuts in two phenomena:
- Constraint Satisfaction Shortcuts: where constraints are satisfied without achieving the intended task.
- Cognition Shortcuts: where biased data leads to semantically incorrect concept mappings despite logically sound inference.
Building on recent matrix-based logic programming semantics, we introduce design elements to mitigate shortcuts, including a unified encoding of rules and constraints in a single matrix. We also identify connections to fuzzy logic t-norms and empirically compare their gradient flow properties. Through carefully designed experiments on MNIST variants, we show that one-to-one grounding of neural outputs to logical atoms significantly reduces both shortcut types compared to previous methods that rely on soft probability distributions. Finally, we confirm that architectural choices in coupling symbolic knowledge with neural learning play a critical role in shortcut mitigation.
Blogger's Review: This research highlights the potential of using differentiable logic programming to overcome reasoning shortcuts in neurosymbolic systems, emphasizing the importance of structural design in achieving a synergy between logical reasoning and neural networks. The effective reasoning mechanisms not only enhance model performance but also improve interpretability, warranting further exploration and application.