Researchers on LessWrong tested a new approach to making AI systems more interpretable. They trained natural language autoencoders—models that generate written explanations of how language models work—with a penalty for verbose output. They found that explanations could be shortened by thirty to forty percent without sacrificing accuracy, and sometimes improved accuracy by cutting words. This suggests current AI explanations contain substantial padding. The finding could reshape how researchers build tools to understand neural networks.

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