Deciphering Neuronal Universality in GPT-2 Language Models

As Large Language Models (LLMs) gain prominence in high-stakes applications, understanding their decision-making processes becomes crucial to mitigate potential risks. The inherent opacity of these models has fueled interpretability research, leveraging the unique advantages of artificial neural networks—being observable and deterministic—for empirical scrutiny. A comprehensive understanding of these models not only enhances our knowledge but […]

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