From LLM to NMT: Advancing Low-Resource Machine Translation with Claude
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Abstract (arXiv)
We show that Claude 3 Opus, a large language model (LLM) released by Anthropic in March 2024, exhibits stronger machine translation competence than other LLMs. Though we find evidence of data contamination with Claude on FLORES-200, we curate new benchmarks that corroborate the effectiveness of Claude for low-resource machine translation into English. We find that Claude has remarkable textitresource efficiency -- the degree to which the quality of the translation model depends on a language pair's resource level. Finally, we show that advancements in LLM translation can be compressed into traditional neural machine translation (NMT) models. Using Claude to generate synthetic data, we demonstrate that knowledge distillation advances the state-of-the-art in Yoruba-English translation, meeting or surpassing strong baselines like NLLB-54B and Google Translate.
Key facts. Cited by 57 papers in the map (data through 7 Oct 2026). Builds on 17 of its 34 references that are in the map. First posted on arXiv 22 Apr 2024.
Builds on
17 of 34 references.
References · 17
- 2020Language Models are Few-Shot Learnerscited by 32,199
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- 2015Distilling the Knowledge in a Neural Networkcited by 10,076
- 2020Scaling Laws for Neural Language Modelscited by 6,411
- 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,290
- 2020COMET: A Neural Framework for MT Evaluationcited by 1,024
- 2016Sequence-Level Knowledge Distillationcited by 761
- 2021The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translationcited by 556
- 2019On the Efficacy of Knowledge Distillationcited by 377
- 2023NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmarkcited by 339
- 2023Prompting Large Language Model for Machine Translation: A Case Studycited by 247
- 2023Large language models effectively leverage document-level context for literary translation, but critical errors persistcited by 80
5 more
- 2019The FLoRes Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-Englishcited by 61
- 2021The Effect of Domain and Diacritics in Yor`ub'a-English Neural Machine Translationcited by 38
- 2022Memory-efficient NLLB-200: Language-specific Expert Pruning of a Massively Multilingual Machine Translation Modelcited by 36
- 2024MT-PATCHER: Selective and Extendable Knowledge Distillation from Large Language Models for Machine Translationcited by 10
- 2020MASRI-HEADSET: A Maltese Corpus for Speech Recognitioncited by 4
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Citing papers per year: 2024: 24; 2025: 24; 2026: 9.
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