Digging Deeper: Learning Multi-Level Concept Hierarchies
arXiv:2603.10084v1 Announce Type: new Abstract: Although concept-based models promise interpretability by explaining predictions with human-understandable concepts, they typically rely on exhaustive annotations and treat concepts as flat and independent. To circumvent this, recent work has introduced Hierarchical Concept Embedding Models (HiCEMs) to explicitly model concept relationships, and Concept Splitting to discover sub-concepts using only coarse annotations. However, both HiCEMs and Concept Splitting are restricted to shallow hierarchies. We overcome this limitation with Multi-Level Concept Splitting (MLCS), which discovers multi-level concept hierarchies from only top-level supervision, and Deep-HiCEMs, an architecture that represents these discovered hierarchies and enables interventions at multiple levels of abstraction. Experiments across multiple datasets show that MLCS discovers human-interpretable concepts absent during training and that Deep-HiCEMs maintain high accurac
arXiv:2603.10084v1 Announce Type: new Abstract: Although concept-based models promise interpretability by explaining predictions with human-understandable concepts, they typically rely on exhaustive annotations and treat concepts as flat and independent. To circumvent this, recent work has introduced Hierarchical Concept Embedding Models (HiCEMs) to explicitly model concept relationships, and Concept Splitting to discover sub-concepts using only coarse annotations. However, both HiCEMs and Concept Splitting are restricted to shallow hierarchies. We overcome this limitation with Multi-Level Concept Splitting (MLCS), which discovers multi-level concept hierarchies from only top-level supervision, and Deep-HiCEMs, an architecture that represents these discovered hierarchies and enables interventions at multiple levels of abstraction. Experiments across multiple datasets show that MLCS discovers human-interpretable concepts absent during training and that Deep-HiCEMs maintain high accuracy while supporting test-time concept interventions that can improve task performance.
Executive Summary
The article introduces Multi-Level Concept Splitting (MLCS) and Deep-HiCEMs, which enable the discovery of multi-level concept hierarchies from top-level supervision and represent these hierarchies for interventions at multiple abstraction levels. Experiments demonstrate the discovery of human-interpretable concepts and maintenance of high accuracy with test-time concept interventions. This approach overcomes limitations of existing models, providing a more nuanced understanding of concept relationships and improving task performance.
Key Points
- ▸ Introduction of Multi-Level Concept Splitting (MLCS) for discovering multi-level concept hierarchies
- ▸ Development of Deep-HiCEMs for representing discovered hierarchies and enabling interventions
- ▸ Experiments demonstrating the discovery of human-interpretable concepts and improved task performance
Merits
Improved Interpretability
MLCS and Deep-HiCEMs provide a more nuanced understanding of concept relationships, enhancing model interpretability
Increased Accuracy
Test-time concept interventions can improve task performance, maintaining high accuracy
Demerits
Limited Scalability
The approach may be limited by the complexity of the hierarchies and the availability of top-level supervision
Expert Commentary
The introduction of MLCS and Deep-HiCEMs marks a significant advancement in the development of interpretable and explainable AI models. By discovering multi-level concept hierarchies and enabling interventions at multiple abstraction levels, this approach has the potential to revolutionize the way we understand and interact with complex machine learning models. However, further research is needed to address scalability and generalizability concerns, as well as to explore the full range of applications and implications.
Recommendations
- ✓ Further investigation into the scalability and generalizability of MLCS and Deep-HiCEMs
- ✓ Exploration of applications in areas like healthcare, finance, and education, where transparency and accountability are crucial