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

Machine Learning Street Talk (MLST)

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Navigating Control in Language Models

This chapter explores the intricacies of controlling language models, focusing on techniques like soft prompting and the behavior of GPT-2. It discusses the interplay between embedding vectors and model outputs, framed through a control theory perspective to illuminate influences on model stability and recovery. The conversation highlights empirical research and the complexities of enhancing language model robustness against adversarial inputs and managing external influences.

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