mlsanityData to 7 Oct 2026

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

arXiv 2408.06537 ↗First posted 13 Aug 2024cs.CLcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 30 of its 44 references that are in the map. First posted on arXiv 13 Aug 2024.

Builds on

30 of 44 references.

References · 30

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18 more
  1. 2022Prompting PaLM for Translation: Assessing Strategies and Performancecited by 144
  2. 2020Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translationcited by 137
  3. 2022Truncation Sampling as Language Model Desmoothingcited by 121
  4. 2021High Quality Rather than High Model Probability: Minimum Bayes Risk Decoding with Neural Metricscited by 104
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  6. 2021Sampling-Based Approximations to Minimum Bayes Risk Decoding for Neural Machine Translationcited by 63
  7. 2023Epsilon Sampling Rocks: Investigating Sampling Strategies for Minimum Bayes Risk Decoding for Machine Translationcited by 57
  8. 2021Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translationcited by 56
  9. 2023Faster Minimum Bayes Risk Decoding with Confidence-based Pruningcited by 34
  10. 2023MBR and QE Finetuning: Training-time Distillation of the Best and Most Expensive Decoding Methodscited by 33
  11. 2024Linear-time Minimum Bayes Risk Decoding with Reference Aggregationcited by 21
  12. 2024Preliminary WMT24 Ranking of General MT Systems and LLMscited by 20
  13. 2023There's no Data Like Better Data: Using QE Metrics for MT Data Filteringcited by 19
  14. 2024Hyperparameter-Free Approach for Faster Minimum Bayes Risk Decodingcited by 15
  15. 2021Scaling Hierarchical Agglomerative Clustering to Billion-sized Datasetscited by 11
  16. 2024MT-PATCHER: Selective and Extendable Knowledge Distillation from Large Language Models for Machine Translationcited by 10
  17. 2023Quality-Aware Translation Models: Efficient Generation and Quality Estimation in a Single Modelcited by 9
  18. 2024Don't Throw Away Data: Better Sequence Knowledge Distillationcited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 6; 2026: 4.

10 papers.

  1. 2025Enhancing Multilingual LLM Pretraining with Model-Based Data Selectioncited by 33
  2. 2025Granary: Speech Recognition and Translation Dataset in 25 European Languagescited by 21
  3. 2025Modeling Annotator Disagreement with Demographic-Aware Experts and Synthetic Perspectivescited by 10
  4. 2026TranslateGemma Technical Reportcited by 7
  5. 2025Extending Automatic Machine Translation Evaluation to Book-Length Documentscited by 6
  6. 2025KIT's Offline Speech Translation and Instruction Following Submission for IWSLT 2025cited by 4
  7. 2025Quality-Aware Decoding: Unifying Quality Estimation and Decodingcited by 1
  8. 2026Consensus Group Relative Policy Optimization for Text Generationcited by 0
  9. 2026SalamandraTA at WMT 2026 Terminology Shared Task: Hard Examples Are Better Teacherscited by 0
  10. 2026Doc2FRC: Length-Consistent Document-Level Machine Translation via Fixed-Range Chunkingcited by 0