@inproceedings{noh-etal-2026-lost,
title = "Lost in Mimicry: Verified Training-Free Post-Editing for {LLM} Translationese",
author = "Noh, Dongwon and
Koh, Donghyeok and
Lim, Yeon-Soo and
Jeong, Seohyeong and
Kim, Yunsu and
Kim, Gyuwan and
Do, Soojong and
Bak, Jinyeong and
Eo, Sugyeong and
Park, Cheoneum",
editor = "Haddow, Barry and
Kocmi, Tom and
Koehn, Philipp and
Monz, Christof",
booktitle = "Proceedings of the Eleventh Conference on Machine Translation",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wmt-main.25/",
pages = "462--491",
ISBN = "979-8-89176-474-3",
abstract = "Translations generated by Large Language Models (LLMs) often exhibit unnatural stylistic traces distinct from human-written translations, which we refer to as LLM translationese. We propose {\textbackslash}textit{\{}MELT{\}}, a training-free post-editing framework for mitigating LLM translationese across five target languages (Korean, Chinese, Japanese, German, and French) without weight updates. MELT defines translationese patterns based on faithfulness, voice, register, naturalness, and surface form, and combines a three-stage verification gate with selective multi-agent debate to preserve meaning and mitigate self-bias. Experiments on FLORES-200 show that verification contributes most to quality improvement and that MELT achieves the lowest calibration error with respect to Oracle preference across the five languages. Our analysis further shows that LLM translationese exhibits language-specific realizations while sharing a common mechanism of English-structure mimicry."
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<abstract>Translations generated by Large Language Models (LLMs) often exhibit unnatural stylistic traces distinct from human-written translations, which we refer to as LLM translationese. We propose \textbackslashtextit{MELT}, a training-free post-editing framework for mitigating LLM translationese across five target languages (Korean, Chinese, Japanese, German, and French) without weight updates. MELT defines translationese patterns based on faithfulness, voice, register, naturalness, and surface form, and combines a three-stage verification gate with selective multi-agent debate to preserve meaning and mitigate self-bias. Experiments on FLORES-200 show that verification contributes most to quality improvement and that MELT achieves the lowest calibration error with respect to Oracle preference across the five languages. Our analysis further shows that LLM translationese exhibits language-specific realizations while sharing a common mechanism of English-structure mimicry.</abstract>
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%0 Conference Proceedings
%T Lost in Mimicry: Verified Training-Free Post-Editing for LLM Translationese
%A Noh, Dongwon
%A Koh, Donghyeok
%A Lim, Yeon-Soo
%A Jeong, Seohyeong
%A Kim, Yunsu
%A Kim, Gyuwan
%A Do, Soojong
%A Bak, Jinyeong
%A Eo, Sugyeong
%A Park, Cheoneum
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Eleventh Conference on Machine Translation
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-474-3
%F noh-etal-2026-lost
%X Translations generated by Large Language Models (LLMs) often exhibit unnatural stylistic traces distinct from human-written translations, which we refer to as LLM translationese. We propose \textbackslashtextit{MELT}, a training-free post-editing framework for mitigating LLM translationese across five target languages (Korean, Chinese, Japanese, German, and French) without weight updates. MELT defines translationese patterns based on faithfulness, voice, register, naturalness, and surface form, and combines a three-stage verification gate with selective multi-agent debate to preserve meaning and mitigate self-bias. Experiments on FLORES-200 show that verification contributes most to quality improvement and that MELT achieves the lowest calibration error with respect to Oracle preference across the five languages. Our analysis further shows that LLM translationese exhibits language-specific realizations while sharing a common mechanism of English-structure mimicry.
%U https://aclanthology.org/2026.wmt-main.25/
%P 462-491Markdown (Informal)
[Lost in Mimicry: Verified Training-Free Post-Editing for LLM Translationese](https://aclanthology.org/2026.wmt-main.25/) (Noh et al., WMT 2026)
ACL
- Dongwon Noh, Donghyeok Koh, Yeon-Soo Lim, Seohyeong Jeong, Yunsu Kim, Gyuwan Kim, Soojong Do, Jinyeong Bak, Sugyeong Eo, and Cheoneum Park. 2026. Lost in Mimicry: Verified Training-Free Post-Editing for LLM Translationese. In Proceedings of the Eleventh Conference on Machine Translation, pages 462–491, Budapest, Hungary. Association for Computational Linguistics.