LSDC: An Efficient and Effective Large-Scale Data Compression Method for Supervised Fine-tuning of Large Language Models

Abstract

With the scale of Large Language Models(LLMs) and the size of the training data continuing to expand, the computational costs required for training or tuning have significantly increased as well. In this work we propose an efficient and effective Large-Scale Data Compression (LSDC) method to substantially reduce the size of training data and thus enhance the training efficiency without compromising the performance of LLMs through a bifurcated quantization strategy. Specifically, our method first segments the dataset into multiple clusters, significantly reducing the time and memory requirements for data compression. Then, during the second phase of coreset selection, the diversity of samples is ensured by maximizing the submodular gain in order to avoid performance degradation. The comparative experiments showed that the performance of LLMs fine-tuned on a 20% compressed subset of the Alpaca dataset using LSDC outperformed those on the full dataset. Moreover,on a domain-specific instruction dataset of millions of samples, the LLMs fine-tuned on a 10% compressed dataset using LSDC outperformed those on the entire dataset, which dramatically enhances the domain-adaption capabilities of LLMs. This provides a promising potential of LSDC in training bigger LLMs from scratch and supervised fine-tuning as well.