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        <title>Guiding Large Language Models to Post-Edit Machine Translation with Error Annotations</title>
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    <abstract>Machine Translation (MT) remains one of the last NLP tasks where large language models (LLMs) have not yet replaced dedicated supervised systems. This work exploits the complementary strengths of LLMs and supervised MT by guiding LLMs to automatically post-edit MT with external feedback on its quality, derived from Multidimensional Quality Metric (MQM) annotations. Working with LLaMA-2 models, we consider prompting strategies varying the nature of feedback provided and then fine-tune the LLM to improve its ability to exploit the provided guidance. Through experiments on Chinese-English, English-German, and English-Russian MQM data, we demonstrate that prompting LLMs to post-edit MT improves TER, BLEU and COMET scores, although the benefits of fine-grained feedback are not clear. Fine-tuning helps integrate fine-grained feedback more effectively and further improves translation quality based on both automatic and human evaluation.</abstract>
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%0 Conference Proceedings
%T Guiding Large Language Models to Post-Edit Machine Translation with Error Annotations
%A Ki, Dayeon
%A Carpuat, Marine
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F ki-carpuat-2024-guiding
%X Machine Translation (MT) remains one of the last NLP tasks where large language models (LLMs) have not yet replaced dedicated supervised systems. This work exploits the complementary strengths of LLMs and supervised MT by guiding LLMs to automatically post-edit MT with external feedback on its quality, derived from Multidimensional Quality Metric (MQM) annotations. Working with LLaMA-2 models, we consider prompting strategies varying the nature of feedback provided and then fine-tune the LLM to improve its ability to exploit the provided guidance. Through experiments on Chinese-English, English-German, and English-Russian MQM data, we demonstrate that prompting LLMs to post-edit MT improves TER, BLEU and COMET scores, although the benefits of fine-grained feedback are not clear. Fine-tuning helps integrate fine-grained feedback more effectively and further improves translation quality based on both automatic and human evaluation.
%R 10.18653/v1/2024.findings-naacl.265
%U https://aclanthology.org/2024.findings-naacl.265
%U https://doi.org/10.18653/v1/2024.findings-naacl.265
%P 4253-4273

Markdown (Informal)

[Guiding Large Language Models to Post-Edit Machine Translation with Error Annotations](https://aclanthology.org/2024.findings-naacl.265) (Ki & Carpuat, Findings 2024)

• Guiding Large Language Models to Post-Edit Machine Translation with Error Annotations (Ki & Carpuat, Findings 2024)

ACL

• Dayeon Ki and Marine Carpuat. 2024. Guiding Large Language Models to Post-Edit Machine Translation with Error Annotations. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 4253–4273, Mexico City, Mexico. Association for Computational Linguistics.