  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 training data for better model
training. Specifically, ClusterClip Sampling utilizes data clustering to
reflect the data distribution of the training set and balances the common
samples and rare samples during training based on the cluster results. A
repetition clip operation is introduced to mitigate the overfitting issue led
by samples from certain clusters. Extensive experiments validate the
effectiveness of ClusterClip Sampling, which outperforms random sampling and
other cluster-based sampling variants under various training datasets and large
language models.
