Prompt Paraphrasing
Treating prompt generation as a paraphrasing task is a method used during the expansion step of prompt optimization. It involves applying off-the-shelf paraphrasing systems, which can be based on Large Language Models or other architectures, to transform an input prompt into new, semantically equivalent candidate prompts to explore the search space.
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Data Science
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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Prompt Paraphrasing
Gradient-based search
Prompt Generation
Prompt Scoring
Exploring and Learning Non-String Prompt Representations
Continuous Prompts (Soft Prompts)
Notation for Hard Prompts and Their Embeddings
A developer is trying to make a language model summarize articles. They provide the model with the following text input for each article: 'Summarize the following text in three sentences: [article text]'. Which of the following statements best analyzes why this input is considered a 'discrete' or 'hard' prompt?
Evaluating a Prompting Strategy for a Customer Service Chatbot
Analyzing the Transformation of a User Prompt
Example of a Hard Prompt
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