From Isolation to Entanglement: When Do Interpretability Methods Identify and Disentangle Known Concepts?

1. 1Evaluate the quality of features using metrics beyond isolation, such as mutual information and correlation
2. 2Apply sparse autoencoders (SAEs) and probes to neural network activations to identify disentangled representations
3. 3Compare the performance of different featurization methods in disentangling known concepts
4. 4Test the robustness of disentangled representations to changes in the neural network architecture or training data
5. 5Analyze the results to determine when interpretability methods can reliably identify and disentangle known concepts

Researchers and practitioners working with neural networks and interpretability methods can benefit from understanding the limitations and capabilities of common featurization methods, such as sparse autoencoders and probes, in disentangling latent concepts

💡 Interpretability methods can identify and disentangle known concepts in neural networks, but their quality evaluation should go beyond isolation and consider the relationships between concepts

🤖 Can interpretability methods disentangle known concepts in neural networks? New research explores the limits of sparse autoencoders and probes 📊