Sparse autoencoders are tools that help us understand how language models work by breaking down their process into simpler parts. They help identify important features in the model that contribute to its outputs.
The idea of sparsity means only a few features are needed to describe something, while superposition lets a lot of different features exist in a small space. This makes learning and processing more efficient for the model.
Using sparse autoencoders opens up new ways to interact with language models. Instead of just inputting text and getting answers, we can manipulate features and explore the model's internal workings more creatively.
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