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

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.

Publication
Findings of the Association for Computational Linguistics: EMNLP 2023
Ahmad Idrissi-Yaghir
Ahmad Idrissi-Yaghir
Researcher in the first cohort

My research interests include Deep Learning, Natural Language Processing, and Information Retrieval.

Christoph M. Friedrich
Christoph M. Friedrich
Principal Investigator

My research interests include Deep Learning, Computer Vision, Radiomics, and Explainable AI.

Jens Kleesiek
Jens Kleesiek
Principal Investigator
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