Abstract
We propose iteratively prompting a large language model to self-correct a translation, with inspiration from their strong language capability as well as a human-like translation approach. Interestingly, multi-turn querying reduces the output’s string-based metric scores, but neural metrics suggest comparable or improved quality after two or more iterations. Human evaluations indicate better fluency and naturalness compared to initial translations and even human references, all while maintaining quality. Ablation studies underscore the importance of anchoring the refinement to the source and a reasonable seed translation for quality considerations. We also discuss the challenges in evaluation and relation to human performance and translationese.- Anthology ID:
- 2024.eamt-1.17
- Volume:
- Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 1)
- Month:
- June
- Year:
- 2024
- Address:
- Sheffield, UK
- Editors:
- Carolina Scarton, Charlotte Prescott, Chris Bayliss, Chris Oakley, Joanna Wright, Stuart Wrigley, Xingyi Song, Edward Gow-Smith, Rachel Bawden, Víctor M Sánchez-Cartagena, Patrick Cadwell, Ekaterina Lapshinova-Koltunski, Vera Cabarrão, Konstantinos Chatzitheodorou, Mary Nurminen, Diptesh Kanojia, Helena Moniz
- Venue:
- EAMT
- SIG:
- Publisher:
- European Association for Machine Translation (EAMT)
- Note:
- Pages:
- 181–190
- Language:
- URL:
- https://aclanthology.org/2024.eamt-1.17
- DOI:
- Cite (ACL):
- Pinzhen Chen, Zhicheng Guo, Barry Haddow, and Kenneth Heafield. 2024. Iterative Translation Refinement with Large Language Models. In Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 1), pages 181–190, Sheffield, UK. European Association for Machine Translation (EAMT).
- Cite (Informal):
- Iterative Translation Refinement with Large Language Models (Chen et al., EAMT 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2024.eamt-1.17.pdf