Self-Instruct Framework, Explained
Motivation
As Large Language Models (LLMs) revolutionize our life, the growth of instruction-tuned LLMs faces significant challenges: the critical need for vast, varied, and high-quality datasets. Traditional methods, such as employing human annotators to generate datasets – a strategy used in InstructGPT (image above)— face high costs, limited diversity, creativity, and allignment challenges. To address these limitations, the Self-Instruct framework² was introduced. Its core idea is simple and powerful: let language models (LM) generate training data, leading to more cost-effective, diverse and creative datasets.
Therefore, in this article, I would like to lead you through the framework step-by-step, demonstrating all the details so that after reading it, you will be able to reproduce the results yourself
