Zero-shot Concept Bottleneck Models
Summary: arXiv:2502.09018v2 Announce Type: replace-cross
Abstract: Concept bottleneck models (CBMs) are inherently interpretable and intervenable neural network models, which explain their final label prediction by the intermediate prediction of high-level semantic concepts. However, they require target task training to learn input-to-concept and concept-to-label mappings, incurring target dataset collections and training resources.
In this paper, we present zero-shot concept bottleneck models (Z-CBMs), which predict concepts and labels in a fully zero-shot manner without training neural networks. Z-CBMs utilize a large-scale concept bank, which is composed of millions of vocabulary extracted from the web, to describe arbitrary input in various domains.
Key Features of Zero-shot Concept Bottleneck Models
- Interpretable and Intervenable: Z-CBMs maintain the interpretability and intervenability of traditional CBMs while eliminating the need for training.
- Dynamic Concept Retrieval: Z-CBMs implement a unique concept retrieval mechanism that enables the model to dynamically find input-related concepts through cross-modal search in the concept bank.
- Sparse Linear Regression: The inference mechanism employs sparse linear regression to select essential concepts from the retrieved data, ensuring that the most relevant concepts are utilized for label prediction.
Advantages of Z-CBMs
The introduction of Z-CBMs marks a significant advancement in the field of machine learning. Here are some of the primary advantages:
- No Training Required: Unlike traditional CBMs, Z-CBMs do not require a training phase, which saves time and resources.
- Scalability: Utilizing a vast concept bank allows Z-CBMs to be applied across various domains without the need for domain-specific datasets.
- High Flexibility: The ability to operate in a zero-shot manner makes Z-CBMs adaptable to new tasks and concepts that were previously unseen during model development.
Experimental Results
Through extensive experiments, the research confirms that Z-CBMs offer interpretable and intervenable concepts without necessitating additional training. The results demonstrate that Z-CBMs can effectively leverage the concept bank to enhance predictive performance across multiple tasks.
Conclusion
The development of zero-shot concept bottleneck models represents a breakthrough in the interpretability and usability of neural networks. By eliminating the need for extensive training and enabling dynamic concept retrieval, Z-CBMs pave the way for more efficient and adaptable AI systems.
For those interested in the technical details, the code for Z-CBMs will be made available at GitHub Repository.