Lost in Literalism: How Supervised Training Shapes Translationese in
  LLMs
Large language models (LLMs) have achieved remarkable success in machine
translation, demonstrating impressive performance across diverse languages.
However, translationese, characterized by overly literal and unnatural
translations, remains a persistent challenge in LLM-based translation systems.
Despite their pre-training on vast corpora of natural utterances, LLMs exhibit
translationese errors and generate unexpected unnatural translations, stemming
from biases introduced during supervised fine-tuning (SFT). In this work, we
systematically evaluate the prevalence of translationese in LLM-generated
translations and investigate its roots during supervised training. We introduce
methods to mitigate these biases, including polishing golden references and
filtering unnatural training instances. Empirical evaluations demonstrate that
these approaches significantly reduce translationese while improving
translation naturalness, validated by human evaluations and automatic metrics.
Our findings highlight the need for training-aware adjustments to optimize LLM
translation outputs, paving the way for more fluent and
target-language-consistent translations. We release the data and code at
https://github.com/yafuly/LLM_Translationese.
67cac954f3efd1be73a870ca
Yafu Li
extracted_pending
2025-03-07T10:24:21.804Z
63f3502a520c14618925825a
/avatars/e986a2a6625e7be6890616a417f908d2.svg
Yafu Li
user
yaful
67cac954f3efd1be73a870cb
Ronghao Zhang
67cac954f3efd1be73a870cc
Zhilin Wang
67cac954f3efd1be73a870cd
Huajian Zhang
67cac954f3efd1be73a870ce
Leyang Cui
67cac954f3efd1be73a870cf
Yongjing Yin
67cac954f3efd1be73a870d0
Tong Xiao
67cac954f3efd1be73a870d1
Yue Zhang
Lost in Literalism: How Supervised Training Shapes Translationese in
  LLMs
Large language models (LLMs) have achieved remarkable success in machine
translation, demonstrating impressive performance across diverse languages.
However, translationese, characterized by overly literal and unnatural
translations, remains a persistent challenge in LLM-based translation systems.
Despite their pre-training on vast corpora of natural utterances, LLMs exhibit
translationese errors and generate unexpected unnatural translations, stemming
from biases introduced during supervised fine-tuning (SFT). In this work, we
systematically evaluate the prevalence of translationese in LLM-generated
translations and investigate its roots during supervised training. We introduce
methods to mitigate these biases, including polishing golden references and
filtering unnatural training instances. Empirical evaluations demonstrate that
these approaches significantly reduce translationese while improving
translation naturalness, validated by human evaluations and automatic metrics.
Our findings highlight the need for training-aware adjustments to optimize LLM
translation outputs, paving the way for more fluent and
target-language-consistent translations. We release the data and code at
https://github.com/yafuly/LLM_Translationese.
Methods to mitigate biases introduced during supervised fine-tuning of large language models significantly reduce translationese, improving translation quality through human and automatic evaluations.
LLMs large language models machine translation translationese biases supervised fine-tuning SFT polishing golden references filtering unnatural training instances human evaluations automatic metrics