@inproceedings{dada-etal-2023-impact,
title = "On the Impact of Cross-Domain Data on {G}erman Language Models",
author = "Dada, Amin and
Chen, Aokun and
Peng, Cheng and
Smith, Kaleb and
Idrissi-Yaghir, Ahmad and
Seibold, Constantin and
Li, Jianning and
Heiliger, Lars and
Friedrich, Christoph and
Truhn, Daniel and
Egger, Jan and
Bian, Jiang and
Kleesiek, Jens and
Wu, Yonghui",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.922",
doi = "10.18653/v1/2023.findings-emnlp.922",
pages = "13801--13813",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T On the Impact of Cross-Domain Data on German Language Models
%A Dada, Amin
%A Chen, Aokun
%A Peng, Cheng
%A Smith, Kaleb
%A Idrissi-Yaghir, Ahmad
%A Seibold, Constantin
%A Li, Jianning
%A Heiliger, Lars
%A Friedrich, Christoph
%A Truhn, Daniel
%A Egger, Jan
%A Bian, Jiang
%A Kleesiek, Jens
%A Wu, Yonghui
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F dada-etal-2023-impact
%X 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.
%R 10.18653/v1/2023.findings-emnlp.922
%U https://aclanthology.org/2023.findings-emnlp.922
%U https://doi.org/10.18653/v1/2023.findings-emnlp.922
%P 13801-13813
Markdown (Informal)
[On the Impact of Cross-Domain Data on German Language Models](https://aclanthology.org/2023.findings-emnlp.922) (Dada et al., Findings 2023)
ACL
- Amin Dada, Aokun Chen, Cheng Peng, Kaleb Smith, Ahmad Idrissi-Yaghir, Constantin Seibold, Jianning Li, Lars Heiliger, Christoph Friedrich, Daniel Truhn, Jan Egger, Jiang Bian, Jens Kleesiek, and Yonghui Wu. 2023. On the Impact of Cross-Domain Data on German Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 13801–13813, Singapore. Association for Computational Linguistics.