Superposition and Polysemanticity in Neural Networks: How High-Dimensional Geometry and Sparsity Allow LLMs to Store More Features Than Dimensions
Superposition and Polysemanticity in Neural Networks: How High-Dimensional Geometry and Sparsity Allow LLMs to Store More Features Than Dimensions In neural network interpretability, one of the most persistent puzzles has been the phenomenon of polysemantic neurons: individual neurons in large language models that activate for seemingly unrelated concepts. A single hidden unit in an MLP layer might fire in response to Python syntax errors, academic citations, and geographic place names. For ye
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