Two Intermediate Translations Are Better Than One: Fine-tuning LLMs for Document-level Translation Refinement

• URL: http://arxiv.org/abs/2504.05614v1
• Date: Tue, 08 Apr 2025 02:08:07 GMT
• Title: Two Intermediate Translations Are Better Than One: Fine-tuning LLMs for Document-level Translation Refinement
• Authors: Yichen Dong, Xinglin Lyu, Junhui Li, Daimeng Wei, Min Zhang, Shimin Tao, Hao Yang,
• Abstract summary: Large language models (LLMs) can enhance translation quality through self-refinement.<n>We build on this idea by extending the refinement from sentence-level to document-level translation.<n>Since sentence-to-sentence (Sent2Sent) and Doc2Doc translation address different aspects of the translation process, we propose fine-tuning LLMs for translation refinement.
• Score: 19.513243503109035
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent research has shown that large language models (LLMs) can enhance translation quality through self-refinement. In this paper, we build on this idea by extending the refinement from sentence-level to document-level translation, specifically focusing on document-to-document (Doc2Doc) translation refinement. Since sentence-to-sentence (Sent2Sent) and Doc2Doc translation address different aspects of the translation process, we propose fine-tuning LLMs for translation refinement using two intermediate translations, combining the strengths of both Sent2Sent and Doc2Doc. Additionally, recognizing that the quality of intermediate translations varies, we introduce an enhanced fine-tuning method with quality awareness that assigns lower weights to easier translations and higher weights to more difficult ones, enabling the model to focus on challenging translation cases. Experimental results across ten translation tasks with LLaMA-3-8B-Instruct and Mistral-Nemo-Instruct demonstrate the effectiveness of our approach.

Related papers

• Multilingual Contextualization of Large Language Models for Document-Level Machine Translation [30.005159724115824]
  Large language models (LLMs) have demonstrated strong performance in sentence-level machine translation. We propose a method to improve LLM-based long-document translation through targeted fine-tuning on high-quality document-level data. Our approach supports multiple translation paradigms, including direct document-to-document and chunk-level translation.
  arXiv  Detail & Related papers  (2025-04-16T14:52:22Z)
• Improving LLM-based Document-level Machine Translation with Multi-Knowledge Fusion [21.533772761328656]
  We propose an enhanced approach by incorporating multiple sources of knowledge, including both the document summarization and entity translation.<n>Our approach achieves an average improvement of 0.8, 0.6, and 0.4 COMET scores over the baseline without extra knowledge.
  arXiv  Detail & Related papers  (2025-03-15T14:18:45Z)
• Speech Translation Refinement using Large Language Models [8.602429274223693]
  This paper investigates how large language models (LLMs) can improve the performance of speech translation by introducing a joint refinement process. Through the joint refinement of speech translation (ST) and automatic speech recognition (ASR) transcription via LLMs, the performance of the ST model is significantly improved. Experimental results on the MuST-C and CoVoST 2 datasets, which include seven translation tasks, demonstrate the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2025-01-25T05:32:42Z)
• LLM-based Translation Inference with Iterative Bilingual Understanding [52.46978502902928]
  We propose a novel Iterative Bilingual Understanding Translation method based on the cross-lingual capabilities of large language models (LLMs)<n>The cross-lingual capability of LLMs enables the generation of contextual understanding for both the source and target languages separately.<n>The proposed IBUT outperforms several strong comparison methods.
  arXiv  Detail & Related papers  (2024-10-16T13:21:46Z)
• TasTe: Teaching Large Language Models to Translate through Self-Reflection [82.83958470745381]
  Large language models (LLMs) have exhibited remarkable performance in various natural language processing tasks. We propose the TasTe framework, which stands for translating through self-reflection. The evaluation results in four language directions on the WMT22 benchmark reveal the effectiveness of our approach compared to existing methods.
  arXiv  Detail & Related papers  (2024-06-12T17:21:21Z)
• (Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Texts [52.18246881218829]
  We introduce a novel multi-agent framework based on large language models (LLMs) for literary translation, implemented as a company called TransAgents. To evaluate the effectiveness of our system, we propose two innovative evaluation strategies: Monolingual Human Preference (MHP) and Bilingual LLM Preference (BLP)
  arXiv  Detail & Related papers  (2024-05-20T05:55:08Z)
• Building Accurate Translation-Tailored LLMs with Language Aware Instruction Tuning [57.323716555996114]
  Off-target translation remains an unsolved problem, especially for low-resource languages. Recent works have either designed advanced prompting strategies to highlight the functionality of translation instructions or exploited the in-context learning ability of LLMs. In this work, we design a two-stage fine-tuning algorithm to improve the instruction-following ability (especially the translation direction) of LLMs.
  arXiv  Detail & Related papers  (2024-03-21T13:47:40Z)
• Enhancing Document-level Translation of Large Language Model via Translation Mixed-instructions [24.025242477280983]
  Existing large language models (LLMs) for machine translation are typically fine-tuned on sentence-level translation instructions. This challenge arises from the issue of sentence-level coverage, where subsequent sentences in the document remain untranslated. We propose an approach that combines sentence-level and document-level translation instructions of varying lengths to fine-tune LLMs.
  arXiv  Detail & Related papers  (2024-01-16T03:28:26Z)
• Adapting Large Language Models for Document-Level Machine Translation [46.370862171452444]
  Large language models (LLMs) have significantly advanced various natural language processing (NLP) tasks. Recent research indicates that moderately-sized LLMs often outperform larger ones after task-specific fine-tuning. This study focuses on adapting LLMs for document-level machine translation (DocMT) for specific language pairs.
  arXiv  Detail & Related papers  (2024-01-12T09:29:13Z)
• Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing [12.843274390224853]
  Large Language Models (LLM's) have demonstrated considerable success in various Natural Language Processing tasks. We show that they have yet to attain state-of-the-art performance in Neural Machine Translation. We propose adapting LLM's as Automatic Post-Editors (APE) rather than direct translators.
  arXiv  Detail & Related papers  (2023-10-23T12:22:15Z)
• Towards Effective Disambiguation for Machine Translation with Large Language Models [65.80775710657672]
  We study the capabilities of large language models to translate "ambiguous sentences" Experiments show that our methods can match or outperform state-of-the-art systems such as DeepL and NLLB in four out of five language directions.
  arXiv  Detail & Related papers  (2023-09-20T22:22:52Z)
• Modeling Context With Linear Attention for Scalable Document-Level Translation [72.41955536834702]
  We investigate the efficacy of a recent linear attention model on document translation and augment it with a sentential gate to promote a recency inductive bias. We show that sentential gating further improves translation quality on IWSLT.
  arXiv  Detail & Related papers  (2022-10-16T03:41:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.