Balanced Data Sampling for Language Model Training with Clustering

arXiv:2402.14526v1 Announce Type: new
Abstract: Data plays a fundamental role in the training of Large Language Models (LLMs). While attention has been paid to the collection and composition of datasets, determining the data sampling strategy in training remains an open question. Most LLMs are trained with a simple strategy, random sampling. However, this sampling strategy ignores the unbalanced nature of training data distribution, which can be sub-optimal. In this paper, we propose ClusterClip Sampling to balance the text distribution of …

abstract arxiv attention clustering collection cs.ai cs.cl data datasets language language model language models language model training large language large language models llms question random role sampling simple strategy training type