---
title: "Who cites Quality or Quantity? On Data — 13 citing papers"
description: "13 papers of the Elamur Research library cite «Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for…"
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---
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# Who cites «Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation»

13 papers of our library cite it (of the 149,439 whose bibliographies we read) · 3 with a page here, the most cited first · [← back to «Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation»](https://md.research.elamur.ai/free/en/papers/iyer-2024-quality-quantity-data-scale-diversity/)

1. **[Scaling Low-Resource MT via Synthetic Data Generation with LLMs](https://md.research.elamur.ai/free/en/papers/gibert-2025-scaling-low-resource-mt-synthetic/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ona de Gibert, Joseph Attieh, Teemu Vahtola, et al.*2025 · 15 citations*
2. **[NiuTrans.LMT: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs](https://md.research.elamur.ai/free/en/papers/luo-2026-niutrans-lmt-inclusive-scalable-multilingual/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yingfeng Luo, Ziqiang Xu, Yuxuan Ouyang, et al.*2026*
3. **[SiniticMTError: A Machine Translation Dataset with Error Annotations for Sinitic Languages](https://md.research.elamur.ai/free/en/papers/liu-2026-siniticmterror-machine-translation-dataset-error/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Hannah Liu, Junghyun Min, Annie En-Shiun Lee, et al.*2026*

10 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/iyer-2024-quality-quantity-data-scale-diversity/) — Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation