# Partially Shuffling the Training Data to Improve Language Models

arXiv: https://arxiv.org/abs/1903.04167 · HTML: https://mlsanity.com/paper/1903.04167/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2019.

## Built on by (most cited first, 1 of 1)

- [Refining Packing and Shuffling Strategies for Enhanced Performance in Generative Language Models](https://mlsanity.com/paper/2408.09621/) (2024)

## Abstract (arXiv)

Although SGD requires shuffling the training data between epochs, currently none of the word-level language modeling systems do this. Naively shuffling all sentences in the training data would not permit the model to learn inter-sentence dependencies. Here we present a method that partially shuffles the training data between epochs. This method makes each batch random, while keeping most sentence ordering intact. It achieves new state of the art results on word-level language modeling on both the Penn Treebank and WikiText-2 datasets.