On the Impact of Cross-Domain Data on German Language Models

TL;DR

This paper investigates the impact of cross-domain versus high-quality data on German language models, showing that diverse datasets lead to better performance across multiple tasks.

Contribution

It introduces a new cross-domain German dataset and demonstrates that training on diverse data yields superior results compared to high-quality data alone.

Findings

Models trained on cross-domain data outperform those trained on quality data.

Performance improvements of up to 4.45% over previous state-of-the-art.

Cross-domain training enhances downstream task performance.

Abstract

Traditionally, large language models have been either trained on general web crawls or domain-specific data. However, recent successes of generative large language models, have shed light on the benefits of cross-domain datasets. To examine the significance of prioritizing data diversity over quality, we present a German dataset comprising texts from five domains, along with another dataset aimed at containing high-quality data. Through training a series of models ranging between 122M and 750M parameters on both datasets, we conduct a comprehensive benchmark on multiple downstream tasks. Our findings demonstrate that the models trained on the cross-domain dataset outperform those trained on quality data alone, leading to improvements up to $4.45\%$ over the previous state-of-the-art. The models are available at https://huggingface.co/ikim-uk-essen