Key Insights and Best Practices on Instruction Tuning

Recently, I’ve been involved in projects related to instruction tuning for large language models(LLMs). I felt it was time to summarize some insights and experiences from this work.

This article, presented in a Q&A format, explores key concepts and considerations in instruction tuning, focusing on eight areas:

• Instruction Tuning for LLMs: What and Why?

• Instruction-Tuning Data: Quality or Quantity?

• How to Ensure High-Quality Data?

• Data Diversity vs. Quality: Which is More Important?

• How to Ensure Data Diversity?

• How to Prevent Task-Specific Fine-Tuning from Compromising General Instruction-Following?

• Which Instruction-Tuning Method Should We Choose for a Specific Task?

• What Details Should be Noted Regarding Fine-Tuning?