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The Role of Expansion in Prompt Diversity
In an automated process for finding effective prompts, an 'expansion' step is used to generate new candidate prompts from an existing set. Consider a scenario where the initial set of prompts are all very similar to each other (e.g., 'Summarize this text,' 'Provide a summary,' 'Give me a summary'). What is the primary risk of starting with such a low-diversity set, and how does the expansion operation specifically address this risk?
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Ch.3 Prompting - 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
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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