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The Self-Instruct process begins by establishing a task pool with an initial set of seed tasks. These foundational tasks are hand-crafted, with each one comprising a specific instruction along with a corresponding input-output sample.

Initialization of the Task Pool in Self-Instruct

In the Self-Instruct cycle, a few existing instructions are drawn from the task pool. These sampled instructions serve as in-context examples to prompt the Large Language Model for the generation of a new, related instruction.

Sampling in Self-Instruct

A research team is initiating a process to enhance a language model's ability to generate Python code from natural language descriptions. For their initial task pool, they gather 1,000 random Python code snippets from public repositories. Based on the principles of initializing this process, what is the primary weakness of their approach?

Analyze the three examples provided below, which are intended for an initial task pool. Which example best represents a complete and effective seed task for this process? Justify your choice by explaining what makes it suitable and why the other two examples are incomplete or less effective.

Evaluating Seed Task Suitability

In a methodology designed to bootstrap a large set of instructional data, the initial 'seed' tasks used to start the process are typically generated automatically by a language model.

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