@inproceedings{feng-etal-2025-tear,
title = "{TE}a{R}: Improving {LLM}-based Machine Translation with Systematic Self-Refinement",
author = "Feng, Zhaopeng and
Zhang, Yan and
Li, Hao and
Wu, Bei and
Liao, Jiayu and
Liu, Wenqiang and
Lang, Jun and
Feng, Yang and
Wu, Jian and
Liu, Zuozhu",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.218/",
pages = "3922--3938",
ISBN = "979-8-89176-195-7",
abstract = "Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT). However, human evaluations reveal that LLM-generated translations still contain various errors. Notably, feeding the error information back into the LLMs can facilitate self-refinement, leading to enhanced translation quality. Motivated by these findings, we introduce TEaR (Translate, Estimate, and Refine), a systematic LLM-based self-refinement framework aimed at bootstrapping translation performance. Our key results show that: 1) TEaR framework enables LLMs to improve their translation quality relying solely on self-feedback, measured by both automatic metrics and Multidimensional Quality Metrics (MQM) scores; 2) TEaR autonomously selects improvements, ensuring a robust translation quality baseline while outperforming both internal refinement and external feedback methods. Error analysis and iterative refinement experiments show its ability to continuously reduce translation errors and enhance overall translation quality. Our code and data are publicly available at https://github.com/fzp0424/self{\_}correct{\_}mt."
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<abstract>Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT). However, human evaluations reveal that LLM-generated translations still contain various errors. Notably, feeding the error information back into the LLMs can facilitate self-refinement, leading to enhanced translation quality. Motivated by these findings, we introduce TEaR (Translate, Estimate, and Refine), a systematic LLM-based self-refinement framework aimed at bootstrapping translation performance. Our key results show that: 1) TEaR framework enables LLMs to improve their translation quality relying solely on self-feedback, measured by both automatic metrics and Multidimensional Quality Metrics (MQM) scores; 2) TEaR autonomously selects improvements, ensuring a robust translation quality baseline while outperforming both internal refinement and external feedback methods. Error analysis and iterative refinement experiments show its ability to continuously reduce translation errors and enhance overall translation quality. Our code and data are publicly available at https://github.com/fzp0424/self_correct_mt.</abstract>
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%0 Conference Proceedings
%T TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement
%A Feng, Zhaopeng
%A Zhang, Yan
%A Li, Hao
%A Wu, Bei
%A Liao, Jiayu
%A Liu, Wenqiang
%A Lang, Jun
%A Feng, Yang
%A Wu, Jian
%A Liu, Zuozhu
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F feng-etal-2025-tear
%X Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT). However, human evaluations reveal that LLM-generated translations still contain various errors. Notably, feeding the error information back into the LLMs can facilitate self-refinement, leading to enhanced translation quality. Motivated by these findings, we introduce TEaR (Translate, Estimate, and Refine), a systematic LLM-based self-refinement framework aimed at bootstrapping translation performance. Our key results show that: 1) TEaR framework enables LLMs to improve their translation quality relying solely on self-feedback, measured by both automatic metrics and Multidimensional Quality Metrics (MQM) scores; 2) TEaR autonomously selects improvements, ensuring a robust translation quality baseline while outperforming both internal refinement and external feedback methods. Error analysis and iterative refinement experiments show its ability to continuously reduce translation errors and enhance overall translation quality. Our code and data are publicly available at https://github.com/fzp0424/self_correct_mt.
%U https://aclanthology.org/2025.findings-naacl.218/
%P 3922-3938
Markdown (Informal)
[TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement](https://aclanthology.org/2025.findings-naacl.218/) (Feng et al., Findings 2025)
ACL
- Zhaopeng Feng, Yan Zhang, Hao Li, Bei Wu, Jiayu Liao, Wenqiang Liu, Jun Lang, Yang Feng, Jian Wu, and Zuozhu Liu. 2025. TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 3922–3938, Albuquerque, New Mexico. Association for Computational Linguistics.