Shortcuts and Identifiability in Concept-based Models from a Neuro-Symbolic Lens

• URL: http://arxiv.org/abs/2502.11245v1
• Date: Sun, 16 Feb 2025 19:45:09 GMT
• Title: Shortcuts and Identifiability in Concept-based Models from a Neuro-Symbolic Lens
• Authors: Samuele Bortolotti, Emanuele Marconato, Paolo Morettin, Andrea Passerini, Stefano Teso,
• Abstract summary: Concept-based Models are neural networks that learn a concept extractor to map inputs to high-level concepts and an inference layer to translate these into predictions. We study this problem by establishing a novel connection between Concept-based Models and reasoning shortcuts (RSs) Specifically, we first extend RSs to the more complex setting of Concept-based Models and then derive theoretical conditions for identifying both the concepts and the inference layer.
• Score: 19.324263034925796
• License:
• Abstract: Concept-based Models are neural networks that learn a concept extractor to map inputs to high-level concepts and an inference layer to translate these into predictions. Ensuring these modules produce interpretable concepts and behave reliably in out-of-distribution is crucial, yet the conditions for achieving this remain unclear. We study this problem by establishing a novel connection between Concept-based Models and reasoning shortcuts (RSs), a common issue where models achieve high accuracy by learning low-quality concepts, even when the inference layer is fixed and provided upfront. Specifically, we first extend RSs to the more complex setting of Concept-based Models and then derive theoretical conditions for identifying both the concepts and the inference layer. Our empirical results highlight the impact of reasoning shortcuts and show that existing methods, even when combined with multiple natural mitigation strategies, often fail to meet these conditions in practice.

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