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What’s the Magic Word? A Control Theory of LLM Prompting.

Machine Learning Street Talk (MLST)

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Exploring Controllability and Flexibility in Language Models

This chapter explores the mathematical foundations of controllability in language models, emphasizing the role of singular values and prompt length in shaping output alignment. It highlights the limitations of model architecture and the expansive token space, all while inviting further discourse on the complexities of prompt engineering.

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