Abstract
The advent of transformers has fueled progress in machine translation. More recently large language models (LLMs) have come to the spotlight thanks to their generality and strong performance in a wide range of language tasks, including translation. Here we show that open-source LLMs perform on par with or better than some state-of-the-art baselines in simultaneous machine translation (SiMT) tasks, zero-shot. We also demonstrate that injection of minimal background information, which is easy with an LLM, brings further performance gains, especially on challenging technical subject-matter. This highlights LLMs’ potential for building next generation of massively multilingual, context-aware and terminologically accurate SiMT systems that require no resource-intensive training or fine-tuning.- Anthology ID:
- 2024.emnlp-main.69
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1192–1207
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.emnlp-main.69/
- DOI:
- 10.18653/v1/2024.emnlp-main.69
- Cite (ACL):
- Roman Koshkin, Katsuhito Sudoh, and Satoshi Nakamura. 2024. LLMs Are Zero-Shot Context-Aware Simultaneous Translators. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 1192–1207, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- LLMs Are Zero-Shot Context-Aware Simultaneous Translators (Koshkin et al., EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.emnlp-main.69.pdf