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Machine Learning Street Talk (MLST) cover image

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

Machine Learning Street Talk (MLST)

NOTE

Challenging nature of steering language models with prompts

The challenge lies in using cross-entropy loss to guide language models towards desired outputs with the shortest prompts. Despite efforts like Fang Check 4, a resume checker, only a few have succeeded due to the complexity involved. The difficulty arises from the unpredictable responses of models like GPT-2, which often generate a set of underscores instead of expected completions. This complexity underscores the need for insights and understanding in navigating the behavior of language models.

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