How to prepare an instruction dataset to fine-tune LLM?

Fine-tuning large language models (LLMs) on custom datasets is a popular technique to adapt these powerful models for specific downstream tasks. However, research has shown that simply providing more training data does not necessarily lead to better fine-tuning performance [1]. In fact, some studies have demonstrated strong fine-tuning results with very small datasets, given high-quality and diverse examples [2]. The key to successfully fine-tuning seems to lie more in carefully curating the instruction dataset rather than maximizing raw size [3]. However, precisely defining data quality and optimal variation for the target domain remains an open question. In this post, I will summarize insights from recent studies on how certain data properties and task similarities impact fine-tuning success.

Defining High Quality Training Data

A critical aspect of preparing datasets for LLM fine-tuning is the careful selection and curation of high-quality training data. The definition of “high-quality” can vary depending on the specific goals and requirements of the project. However, a common thread is the need for data that is relevant, accurate, and diverse, contributing to a model’s ability to understand and generate meaningful, informative responses.