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Mapping the mind of a large language model is a significant challenge in computer science, requiring innovative approaches to understand the internal workings of these complex systems. This endeavor seeks to unveil the intricate relationships between input data, internal processing, and output generation. Understanding this process is crucial for improving model performance, addressing biases, and enhancing overall transparency. The methods involved range from analyzing activation …