AIBullisharXiv – CS AI · Apr 137/10
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Neurons Speak in Ranges: Breaking Free from Discrete Neuronal Attribution
Researchers introduce NeuronLens, a framework that interprets neural networks by analyzing activation ranges rather than individual neurons, addressing the widespread polysemanticity problem in large language models. The range-based approach enables more precise concept manipulation while minimizing unintended degradation to model performance.