Anthropic's new research on 'Extracting Interpretable Features from Claude 3 Sonnet' introduces methods to explain how Claude LLM's artificial neurons fire for lifelike responses to general queries.
The study reveals that concepts in LLMs are represented across many neurons, and each neuron represents many concepts, requiring a complex process involving sparse auto-encoders and dictionary learning to understand neuron activation patterns.
Neuron patterns are grouped into 'features' associated with words or concepts, spanning from specific nouns to abstract ideas, representing the same concept across various languages and communication modes.
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