Abstract
We propose on-the-fly ensembling of a neural machine translation (NMT) model with a large language model (LLM), prompted on the same task and input. Through experiments on 4 language directions with varying data amounts, we find that a slightly weaker-at-translation LLM can improve translations of a NMT model, and such an ensemble can produce better translations than ensembling two stronger NMT models.We demonstrate that our ensemble method can be combined with various techniques from LLM prompting, such as in context learning and translation context.- Anthology ID:
- 2024.findings-naacl.35
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2024
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 520–532
- Language:
- URL:
- https://aclanthology.org/2024.findings-naacl.35
- DOI:
- Cite (ACL):
- Hieu Hoang, Huda Khayrallah, and Marcin Junczys-Dowmunt. 2024. On-the-Fly Fusion of Large Language Models and Machine Translation. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 520–532, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- On-the-Fly Fusion of Large Language Models and Machine Translation (Hoang et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/ingestion-checklist/2024.findings-naacl.35.pdf