Introducing the NewsPaLM MBR and QE Dataset: LLM-Generated High-Quality Parallel Data Outperforms Traditional Web-Crawled Data

Mara Finkelstein, David Vilar, Markus Freitag

Abstract
Recent research in neural machine translation (NMT) has shown that training on high-quality machine-generated data can outperform training on human-generated data. This work accompanies the first-ever release of a LLM-generated, MBR-decoded and QE-reranked dataset with both sentence-level and multi-sentence examples. We perform extensive experiments to demonstrate the quality of our dataset in terms of its downstream impact on NMT model performance. We find that training from scratch on our (machine-generated) dataset outperforms training on the (web-crawled) WMT’23 training dataset (which is 300 times larger), and also outperforms training on the top-quality subset of the WMT’23 training dataset. We also find that performing self-distillation by finetuning the LLM which generated this dataset outperforms the LLM’s strong few-shot baseline. These findings corroborate the quality of our dataset, and demonstrate the value of high-quality machine-generated data in improving performance of NMT models.
Anthology ID:
2024.wmt-1.126
Volume:
Proceedings of the Ninth Conference on Machine Translation
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Barry Haddow, Tom Kocmi, Philipp Koehn, Christof Monz
Venue:
WMT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1355–1372
Language:
URL:
https://aclanthology.org/2024.wmt-1.126
DOI:
10.18653/v1/2024.wmt-1.126
Bibkey:
Cite (ACL):
Mara Finkelstein, David Vilar, and Markus Freitag. 2024. Introducing the NewsPaLM MBR and QE Dataset: LLM-Generated High-Quality Parallel Data Outperforms Traditional Web-Crawled Data. In Proceedings of the Ninth Conference on Machine Translation, pages 1355–1372, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Introducing the NewsPaLM MBR and QE Dataset: LLM-Generated High-Quality Parallel Data Outperforms Traditional Web-Crawled Data (Finkelstein et al., WMT 2024)
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https://preview.aclanthology.org/dois-2013-emnlp/2024.wmt-1.126.pdf
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