VentureBeat July 26, 2024
Large language models (LLMs) have made remarkable progress in recent years. But understanding how they work remains a challenge and scientists at artificial intelligence labs are trying to peer into the black box.
One promising approach is the sparse autoencoder (SAE), a deep learning architecture that breaks down the complex activations of a neural network into smaller, understandable components that can be associated with human-readable concepts.
In a new paper, researchers at Google DeepMind introduce JumpReLU SAE, a new architecture that improves the performance and interpretability of SAEs for LLMs. JumpReLU makes it easier to identify and track individual features in LLM activations, which can be a step toward understanding how LLMs learn and reason.
The challenge of interpreting LLMs
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