<ul data-eligibleForWebStory="false"><li>Sparse autoencoders (SAEs) and transcoders are important tools for machine learning interpretability.</li><li>Measuring the interpretability of SAEs remains challenging due to the lack of consensus on benchmarks.</li><li>Current evaluation procedures involve generating single-sentence explanations for each latent, which complicates the assessment process.</li><li>A new method has been proposed to assess the interpretability of sparse coders without the need for natural language explanations, aiming for a more direct evaluation approach.</li></ul>

Evaluating SAE interpretability without explanations

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