Author(s): Youssef Hosni Originally published on Towards AI. Instruction tuning is a process used to enhance large language models (LLMs) by refining their ability to follow specific instructions. OpenAI’s work on InstructGPT first introduced instruction fine-tuning. InstructGPT was trained to follow human instructions better by fine-tuning GPT-3 on datasets where humans rated the model’s responses, which was a major step towards producing ChatGPT. In this article, you’ll learn about the process of instruction fine-tuning to improve the performance of an existing LLM for your specific use case. You’ll also learn about important metrics that can be used to evaluate the performance of your finetuned LLM and quantify its improvement over the base model you started with. Fine-tuning LLMs with Instruction PromptsThe Process of Instruction Fine-TuningPreparing Instruction Data SetsInstruction Fine-Tuning ProcessEvaluation and Performance Metrics Most insights I share in Medium have previously been shared in my weekly newsletter, To Data & Beyond. If you want to be up-to-date with the frenetic world of AI while also feeling inspired to take action or, at the very least, to be well-prepared for the future ahead of us, this is for you. 🏝Subscribe below🏝 to become an AI leader among your peers and receive content not present in any other platform, including Medium: Data Science, Machine Learning, AI, and… Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI
↧