Abstract
While large language models have made remarkable advancements in natural language generation, their potential in machine translation, especially when fine-tuned, remains under-explored. In our study, we conduct comprehensive experiments, evaluating 15 publicly available language models on machine translation tasks. We compare the performance across three methodologies: zero-shot prompting, few-shot learning, and fine-tuning. Central to our approach is the use of QLoRA, an efficient fine-tuning method. On French-English, QLoRA fine-tuning outperforms both few-shot learning and models trained from scratch. This superiority is highlighted in both sentence-level and document-level translations, with a significant BLEU score improvement of 28.93 over the prompting method. Impressively, with QLoRA, the enhanced performance is achieved by fine-tuning a mere 0.77% of the model’s parameters.- Anthology ID:
- 2023.wmt-1.43
- Volume:
- Proceedings of the Eighth Conference on Machine Translation
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Philipp Koehn, Barry Haddow, Tom Kocmi, Christof Monz
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 468–481
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2023.wmt-1.43/
- DOI:
- 10.18653/v1/2023.wmt-1.43
- Cite (ACL):
- Xuan Zhang, Navid Rajabi, Kevin Duh, and Philipp Koehn. 2023. Machine Translation with Large Language Models: Prompting, Few-shot Learning, and Fine-tuning with QLoRA. In Proceedings of the Eighth Conference on Machine Translation, pages 468–481, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Machine Translation with Large Language Models: Prompting, Few-shot Learning, and Fine-tuning with QLoRA (Zhang et al., WMT 2023)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2023.wmt-1.43.pdf