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Prompt Expansion via Feedback
The quality of generated prompts during the expansion phase can be enhanced by incorporating a feedback mechanism. A Large Language Model evaluates an input prompt and generates feedback, which is then used to revise the prompt. This feedback-and-revision cycle repeats until the prompt converges or reaches a desired quality level.
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Foundations of Large Language Models
Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Example of a Prompt for LLM-based Prompt Expansion
Iterative LLM-Based Prompt Search
A team is using an automated process to discover a high-performing prompt for a text summarization task. They begin with an initial set of prompts:
C = {'Summarize the following text for me.', 'Give me the main points of this article.'}. They then apply a single 'expansion' operation, which uses a large language model to generate a new set of candidate prompts based on the ones inC. Which of the following best represents a plausible output set from this single expansion operation?The Role of Expansion in Prompt Diversity
Expansion Function in Search Algorithms
Troubleshooting a Prompt Optimization Process
Prompt Expansion via Edit Operations
Prompt Expansion via Feedback
Prompt Paraphrasing