The paper proposes a method to identify and interpret the directions in activation space of neural networks, addressing the issue of polysemanticity. The method uses sparse autoencoders to reconstruct internal activations and achieves more interpretable and monosemantic results compared to alternative approaches. This method can enable precise model editing and improve model transparency and steerability.

https://arxiv.org/abs//2309.08600

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