Right for the Right Reasons: Avoiding Reasoning Shortcuts via Prototype-Augmented Neurosymbolic AI
Luca Andolfi, Eleonora Giunchiglia
Neurosymbolic methods combine neural perception with symbolic reasoning, but recent studies have shown that state-of-the-art approaches can fall prey of reasoning shortcuts---spurious but predictive associations between learned concepts and target labels that satisfy constraints syntactically, yet violate the intended semantics. In this paper, we investigate the use of prototype-augmented neural architectures to mitigate reasoning shortcuts. By adopting prototypical networks as a backbone, we introduce a method that anchors intermediate concepts in interpretable embeddings and guides their training using the available limited supervision and the symbolic background knowledge. Extensive experiments demonstrate that our approach significantly improves semantic alignment while maintaining predictive performance across multiple benchmarks.