@inproceedings{chen-etal-2024-iterative,
title = "Iterative Translation Refinement with Large Language Models",
author = "Chen, Pinzhen and
Guo, Zhicheng and
Haddow, Barry and
Heafield, Kenneth",
editor = "Scarton, Carolina and
Prescott, Charlotte and
Bayliss, Chris and
Oakley, Chris and
Wright, Joanna and
Wrigley, Stuart and
Song, Xingyi and
Gow-Smith, Edward and
Bawden, Rachel and
S{\'a}nchez-Cartagena, V{\'\i}ctor M and
Cadwell, Patrick and
Lapshinova-Koltunski, Ekaterina and
Cabarr{\~a}o, Vera and
Chatzitheodorou, Konstantinos and
Nurminen, Mary and
Kanojia, Diptesh and
Moniz, Helena",
booktitle = "Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 1)",
month = jun,
year = "2024",
address = "Sheffield, UK",
publisher = "European Association for Machine Translation (EAMT)",
url = "https://aclanthology.org/2024.eamt-1.17",
pages = "181--190",
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.",
}
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%0 Conference Proceedings
%T Iterative Translation Refinement with Large Language Models
%A Chen, Pinzhen
%A Guo, Zhicheng
%A Haddow, Barry
%A Heafield, Kenneth
%Y Scarton, Carolina
%Y Prescott, Charlotte
%Y Bayliss, Chris
%Y Oakley, Chris
%Y Wright, Joanna
%Y Wrigley, Stuart
%Y Song, Xingyi
%Y Gow-Smith, Edward
%Y Bawden, Rachel
%Y Sánchez-Cartagena, Víctor M.
%Y Cadwell, Patrick
%Y Lapshinova-Koltunski, Ekaterina
%Y Cabarrão, Vera
%Y Chatzitheodorou, Konstantinos
%Y Nurminen, Mary
%Y Kanojia, Diptesh
%Y Moniz, Helena
%S Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 1)
%D 2024
%8 June
%I European Association for Machine Translation (EAMT)
%C Sheffield, UK
%F chen-etal-2024-iterative
%X 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.
%U https://aclanthology.org/2024.eamt-1.17
%P 181-190
Markdown (Informal)
[Iterative Translation Refinement with Large Language Models](https://aclanthology.org/2024.eamt-1.17) (Chen et al., EAMT 2024)
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).