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

2025-02-16

AI Summary

This work addresses the interpretability loss and out-of-distribution failure in conceptual models caused by reasoning shortcuts. We first extend the notion of reasoning shortcuts to the concept learning paradigm and establish a theoretical framework for joint identifiability of concept extractors and reasoning layers under a neuro-symbolic perspective. We derive rigorous sufficient conditions ensuring such joint identifiability and prove that mainstream concept learning methods generally violate them in practice. Through neuro-symbolic modeling, identifiability-theoretic analysis, concept disentanglement experiments, and multi-strategy ablation studies, we empirically demonstrate that existing approaches remain vulnerable to shortcut exploitation—even when integrating diverse mitigation strategies—yielding concept representations that are neither truly identifiable nor reliably interpretable. Our core contribution is the formal establishment of a theoretical benchmark for concept-reasoning joint identifiability and the proposal of a novel paradigm for trustworthy concept modeling.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Machine Learning: Neuro-Symbolic Learning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• investigates reasoning shortcuts in Concept-based Models
• derives conditions for concept and inference layer identifiability
• evaluates effectiveness of existing mitigation strategies

Innovation

• Extend reasoning shortcuts to concept-based models
• Derive theoretical conditions for identifiability
• Evaluate existing methods with mitigation strategies