Unlocking Neural Network Secrets: The Rising Role of Sparse Autoencoders

Discover the rising role of Sparse Autoencoders (SAE) in making powerful machine learning models understandable. Learn how SAE breaks down neural networks.

In explaining machine learning models, Sparse Autoencoders (SAE) are becoming increasingly common tools (although they have been around since about 1997).

Machine learning models and LLMs are becoming more powerful and useful, but they are still black boxes, and we don't understand how they perform tasks.

Understanding their workings would be very beneficial.

SAE helps break down the model's calculations into understandable components.

Recently, LLM interpretability researcher Adam Karvonen published a blog post explaining how SAE works.