Mosaic-IT: Cost-Free Compositional Data Synthesis for Instruction Tuning

TL;DR

Mosaic-IT introduces a cost-effective, data augmentation method for instruction tuning of large language models by synthesizing diverse instruction-response pairs without human or teacher model intervention, improving performance and efficiency.

Contribution

It presents Mosaic-IT, a novel compositional data synthesis approach that enhances instruction tuning without additional human or teacher model data generation.

Findings

Achieves consistent performance improvements across benchmarks.

Reduces training costs by 80%.

Enhances multi-step instruction-following skills.

Abstract

Finetuning large language models with a variety of instruction-response pairs has enhanced their capability to understand and follow instructions. Current instruction tuning primarily relies on teacher models or human intervention to generate and refine the instructions and responses for training, which are costly, non-sustainable, and may lack diversity. In this paper, we introduce Mosaic Instruction Tuning (Mosaic-IT), a human/model-free compositional data synthesis method that can efficiently create rich and diverse augmentations from existing instruction tuning data to enhance the LLMs. Mosaic-IT randomly concatenates multiple instruction data into one and trains the model to produce the corresponding responses with predefined higher-level meta-instructions to strengthen its multi-step instruction-following and format-following skills. Our extensive evaluations demonstrate a superior performance and training efficiency of Mosaic-IT, which achieves consistent performance improvements over various benchmarks and an 80% reduction in training costs compared with original instruction tuning. Our codes and data are available at https://github.com/tianyi-lab/Mosaic-IT.