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Anthropic has unveiled a groundbreaking paper that delves into the internal workings of a Large Language Model (LLM), offering unprecedented insights into the previously mysterious “black box” nature of these models. By employing a technique called “dictionary learning,” the research team successfully mapped the internal states of Claude 3 Sonnet, isolating patterns of neuron activations and representing complex model states with fewer active features. This innovative approach revealed a conceptual map within the model, showing how features related to similar concepts, such as “inner conflict,” cluster together. Even more astonishing, the researchers found that by manipulating these features, they could alter the model’s behavior—an advancement with significant implications for AI safety. This study represents a major leap in understanding and potentially controlling LLMs, though challenges remain in fully mapping and leveraging these features for practical safety applications.