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LLM-based Prompt Generation from Input-Output Examples
An alternative method for generating initial prompts with a Large Language Model (LLM) involves providing the model with examples of task inputs and their corresponding desired outputs. This approach relies on the LLM's ability to infer the task's underlying instruction from these concrete examples in order to formulate an effective prompt, rather than being given an abstract task description.
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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
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Manual Creation of Initial Prompts
LLM-based Prompt Generation from Input-Output Examples
LLM-based Prompt Generation from a Task Description
Comparison of LLM-based Prompt Generation Methods for Initialization
A development team is tasked with finding the optimal prompt for a novel and complex text classification task. They plan to use an automated, multi-stage process that repeatedly evaluates, refines, and generates new prompts. Given that the team has very little prior experience with what constitutes a good prompt for this specific task, which of the following actions is the most critical first step to ensure their automated search process is effective?
Starting a Prompt Optimization Project
An automated, iterative process is often used to discover effective prompts. Arrange the following fundamental stages of this process into the correct chronological order.
Learn After
Example of a Prompt Template for Generating Instructions from Input-Output Pairs
Instruction Inference from Input-Output Pairs
A developer wants a language model to generate a clear instruction for the task of 'summarizing a long paragraph into a single, concise sentence.' To do this, they will provide the model with a set of input-output examples and ask it to infer the instruction. Which of the following sets of examples is most likely to result in the desired instruction?
Diagnosing Issues in Instruction Inference
Crafting Examples for Instruction Inference