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An example of a prompt template used to generate a new instruction in the Self-Instruct process is: "You are provided several different instructions for performing some tasks. Please generate an instruction based on these. Task 1: $${\mathrm{instruction}1}$$ Task 2: $${\mathrm{instruction}2}$$ Task 3: $${\mathrm{instruction}3}$$ Task 4: $${\mathrm{instruction}4}$$ New Task:". This template directs the Large Language Model to synthesize a new task instruction by learning from the provided examples in-context.

Example of a Prompt Template for Instruction Generation in Self-Instruct

A research team is using a large language model to generate new, diverse programming-related tasks. They provide the model with a seed set of instructions like "Write a Python function to sort a list" and "Create a SQL query to find all users from a specific country." However, the model consistently outputs slight variations of the *first* example instruction it sees, rather than generating genuinely new and different programming tasks. The team is using the following prompt template, which precedes the list of example instructions: "Here are some programming tasks. Please complete the first task in the list." Based on the principles of effective instruction generation, evaluate why this prompt template is failing to produce novel and diverse instructions. What specific change would you make to the template to better guide the model?

Evaluating a Prompt Template for Instruction Generation

Imagine you are guiding a large language model to create a new task. You will provide it with the following three examples of existing tasks:
1. 'Classify the sentiment of this movie review as positive, negative, or neutral.'
2. 'Summarize the main points of the provided article in three sentences.'
3. 'Translate the following English sentence into French.'

Your goal is to have the model generate a *fourth*, completely new task that is distinct from the examples but follows their general format. Write the single, direct instruction you would place *before* these examples to achieve this goal.

Crafting a Directive for Task Generation

A machine learning engineer wants to use a large language model to expand a dataset of task instructions. The goal is to generate a new, unique instruction that is stylistically and thematically similar to a small set of existing instructions. Which of the following prompts, when combined with the set of existing instructions, is best designed to achieve this specific goal?

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