The study also demonstrates that advancements in LLM translation can be integrated into traditional neural machine translation (NMT) models. By using Claude to generate synthetic data, the authors show that knowledge distillation can improve the state-of-the-art in Yoruba-English translation, matching or even surpassing strong baselines like NLLB-54B and Google Translate.
Key takeaways:
- Claude 3 Opus, a large language model (LLM) by Anthropic, shows stronger machine translation competence than other LLMs.
- Despite evidence of data contamination, Claude proves effective for low-resource machine translation into English.
- Claude exhibits remarkable resource efficiency in translation model quality depending on a language pair's resource level.
- Advancements in LLM translation can be compressed into traditional neural machine translation models, with Claude's synthetic data advancing the state-of-the-art in Yoruba-English translation.