Extracting Concepts from GPT-4
OpenAI Blog
Researchers used scaled sparse autoencoders to extract 16 million distinct computational patterns from GPT-4's operations. The technique identified 16 million individual concepts that the model uses during processing. This capability enables better understanding of how large language models compute internally and may improve interpretability of AI systems.
Why it matters
Using new techniques for scaling sparse autoencoders, we automatically identified 16 million patterns in GPT-4's computations.
