Lost in Literalism: How Supervised Training Shapes Translationese in
  LLMs
Authors: name: Yafu Li, name: Ronghao Zhang, name: Zhilin Wang, name: Huajian Zhang, name: Leyang Cui, name: Yongjing Yin, name: Tong Xiao, name: Yue Zhang

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.