The J++ Lens improves upon the J-Lens by filtering noisy gradients to enhance the accuracy of reading intermediate variables from language model activations. It achieves a 55% success rate in latent variable extraction tasks, outperforming the J-Lens (36%) and R-Lens (38%) with better performance at earlier layers.
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The J++ Lens provides more accurate and reliable extraction of intermediate variables from language model activations, which can be useful for monitoring and detecting unverbalized reasoning patterns.
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