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A researcher is optimizing a soft prompt. With the original, long context, the model predicts the correct answer with 60% probability and a plausible alternative with 30% probability. The researcher's goal is to create a soft prompt that causes the model to predict the correct answer with over 95% probability, even if this significantly changes the probability of the alternative answer. Which optimization approach is better suited for this specific goal?
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
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Formula for Soft Prompt Optimization by Minimizing KL Divergence
A team is creating a soft prompt to summarize a complex user manual for a question-answering model. Their main objective is not just to get the single correct answer, but to ensure the model's uncertainty and its ranking of other plausible-but-incorrect answers are the same with the soft prompt as they were with the full manual. Which of the following optimization strategies best aligns with this specific objective?
Choosing an Optimization Strategy for Soft Prompts
A researcher is optimizing a soft prompt. With the original, long context, the model predicts the correct answer with 60% probability and a plausible alternative with 30% probability. The researcher's goal is to create a soft prompt that causes the model to predict the correct answer with over 95% probability, even if this significantly changes the probability of the alternative answer. Which optimization approach is better suited for this specific goal?