Beyond Slow SAEs: 4 Faster Methods for LLM Interpretability
furongh · x · 2026-08-11
While Sparse Autoencoders (SAEs) are the standard tool for finding interpretable directions in LLMs, they suffer from slow and costly training. The author highlights 4 much faster alternatives:
- ICA Lens (new paper): Uses Independent Component Analysis (ICA) to find non-Gaussian directions, interpreting models without training another dictionary.
- SVD: Uses Singular Value Decomposition as a fast interpretability method for Transformers.
- Sparse weight decomposition: Efficiently extracts model circuits.
- Neuron basis sparsity: Leverages the inherent sparsity in the neuron basis.
The author argues that building new dictionaries is actually easy and these newer methods are almost free compared to mediocre and slow SAEs. However, auto-annotating the items inside the dictionary remains the real challenge.
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