title: Lost in Literalism: How Supervised Training Shapes Translationese in
    LLMs
summary: 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.
paper_authors._id: 67cac954f3efd1be73a870ca
paper_authors.name: Yafu Li
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-03-07T10:24:21.804Z
paper_authors._id: 63f3502a520c14618925825a
paper_authors.avatarUrl: /avatars/e986a2a6625e7be6890616a417f908d2.svg
paper_authors.fullname: Yafu Li
paper_authors.type: user
paper_authors.user: yaful
paper_authors._id: 67cac954f3efd1be73a870cb
paper_authors.name: Ronghao Zhang
paper_authors._id: 67cac954f3efd1be73a870cc
paper_authors.name: Zhilin Wang
paper_authors._id: 67cac954f3efd1be73a870cd
paper_authors.name: Huajian Zhang
paper_authors._id: 67cac954f3efd1be73a870ce
paper_authors.name: Leyang Cui
paper_authors._id: 67cac954f3efd1be73a870cf
paper_authors.name: Yongjing Yin
paper_authors._id: 67cac954f3efd1be73a870d0
paper_authors.name: Tong Xiao
paper_authors._id: 67cac954f3efd1be73a870d1
paper_authors.name: Yue Zhang
paper_title: Lost in Literalism: How Supervised Training Shapes Translationese in
    LLMs
paper_summary: 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.
paper_ai_keywords: Large language models (LLMs) machine translation translationese natural utterances supervised fine-tuning (SFT) golden references training-aware adjustments fluency target-language-consistent