SAE and TensorLens: The Age of Feature Interpretability
Individual neurons are uninterpretable. Sparse Autoencoders extract monosemantic features from model internals, and TensorLens analyzes the entire Transformer as a single unified tensor.
SAE and TensorLens: The Age of Feature Interpretability
In the previous two posts, we:
- Logit/Tuned Lens: Read the model's intermediate predictions
- Activation Patching: Traced which activations are causally responsible for the answer
But here we hit a fundamental problem:
Related Posts
TransformerLens in Practice: Reading Model Circuits with Activation Patching
Using TransformerLens to directly manipulate model activations, we trace which layers and heads causally produce the answer. A hands-on guide to activation patching.
From Logit Lens to Tuned Lens: Reading the Intermediate Thoughts of Transformers
What happens inside an LLM between input and output? Logit Lens and Tuned Lens let us observe how Transformers build predictions layer by layer.
We Benchmarked MiniCPM-o 4.5 in Korean. Here's What Actually Happens.
We benchmarked MiniCPM-o 4.5's Korean performance side by side with English. Image descriptions, OCR, document extraction — what works, what breaks, and why the root cause is architecture, not prompts.