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Explore the interpretability of large language models through this comprehensive lecture that reviews foundational concepts and introduces probing techniques. Begin with an extensive course review and preview section that establishes the theoretical framework for understanding how LLMs process and represent information internally. Delve into probing methodologies, learning how researchers investigate the internal representations and decision-making processes of neural language models to better understand their behavior and capabilities. Gain insights into current approaches for making black-box language models more transparent and interpretable, essential knowledge for anyone working with or researching large-scale language AI systems.
