Key Insights and Best Practices on Instruction Tuning

Author(s): Florian June

Originally published on Towards AI.

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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?

Instruction tuning is the process of further training LLMs on a dataset of (INSTRUCTION, OUTPUT) pairs in a supervised manner, where INSTRUCTION represents the human instruction for the model and OUTPUT represents the desired output that follows the INSTRUCTION. This process aligns the next-word prediction objective of LLMs with the goal of making them follow human instructions.

The process is shown in Figure 1.

Figure 1: General pipeline of instruction tuning. Source: Instruction Tuning for Large Language Models: A Survey.

A key challenge for LLMs is the gap between their training goals and user expectations. LLMs are typically trained… Read the full blog for free on Medium.

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Published via Towards AI