Can an Old Dog Be Taught New Tricks? Taking LLMs Beyond Sentence Level Translation

• URL: http://arxiv.org/abs/2607.14040v1
• Date: Wed, 15 Jul 2026 17:10:24 GMT
• Title: Can an Old Dog Be Taught New Tricks? Taking LLMs Beyond Sentence Level Translation
• Abstract summary: We present PAT (Pragmatic Auto-Translator), a RAG-based system that pairs user-configured specifications with context from a corpus of authentic longform texts in U.S. English and Latin American Spanish.<n>The goal is draft translation for professional verification: target texts reformulated to fit their Spanish-language context, where discourse organization, rhetorical style, and pragmatic norms differ meaningfully from English.<n>Results show that a limited prompt produced no meaningful reformulation, and specifications and corpus-informed translations at times showed substantial reformulation, though not always to effect.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Automatic translation systems, from CAT tools to MT, overwhelmingly treat translation as a sentence-by-sentence act. This paper asks whether LLMs can be moved beyond that paradigm through whole-document, corpus-informed translation. We present PAT (Pragmatic Auto-Translator), a RAG-based system that pairs user-configured specifications with context from a comparable corpus of authentic longform texts in U.S. English and Latin American Spanish, passing retrieved paragraph-, section-, and document-level examples to an LLM for whole-document generation. The goal is draft translation for professional verification: target texts reformulated to fit their Spanish-language context, where discourse organization, rhetorical style, and pragmatic norms differ meaningfully from English. We evaluated six automatic translations of essays on generative AI across three projects using a customized MQM typology, assessed by two trained evaluators working from U.S. English into LATAM and Mexican Spanish. Results show that a limited prompt produced no meaningful reformulation, and specifications and corpus-informed translations at times showed substantial reformulation, though not always to effect. We find that LLMs can be moved toward reformulation and away from the sentence-by-sentence paradigm, though more work is needed to improve the effectiveness of those reformulations. In this paper, we discuss considerations related to automatic translation system design, corpus construction, and translation quality evaluation methodology and results.

Related papers

• Automated evaluation of LLMs for effective machine translation of Mandarin Chinese to English [8.653960569736904]
  We utilise an automated machine learning framework featuring semantic and sentiment analysis to assess Mandarin Chinese to English translation.<n>We compare original and translated texts in various classes of high-profile Chinese texts, which include novel texts that span modern and classical literature, as well as news articles.<n>Our results indicate that the LLMs perform well in news media translation, but show divergence in their performance when applied to literary texts.
  arXiv  Detail & Related papers  (2026-02-15T22:43:06Z)
• Liaozhai through the Looking-Glass: On Paratextual Explicitation of Culture-Bound Terms in Machine Translation [70.43884512651668]
  We formalize Genette's (1987) theory of paratexts from literary and translation studies to introduce the task of paratextual explicitation for machine translation.<n>We construct a dataset of 560 expert-aligned paratexts from four English translations of the classical Chinese short story collection Liaozhai.<n>Our findings demonstrate the potential of paratextual explicitation in advancing machine translation beyond linguistic equivalence.
  arXiv  Detail & Related papers  (2025-09-27T16:27:36Z)
• Two Intermediate Translations Are Better Than One: Fine-tuning LLMs for Document-level Translation Refinement [19.513243503109035]
  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.
  arXiv  Detail & Related papers  (2025-04-08T02:08:07Z)
• Lost in Literalism: How Supervised Training Shapes Translationese in LLMs [51.04435855143767]
  Large language models (LLMs) have achieved remarkable success in machine translation.<n>However, translationese, characterized by overly literal and unnatural translations, remains a persistent challenge.<n>We introduce methods to mitigate these biases, including polishing golden references and filtering unnatural training instances.
  arXiv  Detail & Related papers  (2025-03-06T12:14:45Z)
• 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 [56.7988577327046]
  We introduce TransAgents, a novel multi-agent framework that simulates the roles and collaborative practices of a human translation company.<n>Our findings highlight the potential of multi-agent collaboration in enhancing translation quality, particularly for longer texts.
  arXiv  Detail & Related papers  (2024-05-20T05:55:08Z)
• Machine Translation with Large Language Models: Prompt Engineering for Persian, English, and Russian Directions [0.0]
  Generative large language models (LLMs) have demonstrated exceptional proficiency in various natural language processing (NLP) tasks. We conducted an investigation into two popular prompting methods and their combination, focusing on cross-language combinations of Persian, English, and Russian.
  arXiv  Detail & Related papers  (2024-01-16T15:16:34Z)
• 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)
• ParroT: Translating during Chat using Large Language Models tuned with Human Translation and Feedback [90.20262941911027]
  ParroT is a framework to enhance and regulate the translation abilities during chat. Specifically, ParroT reformulates translation data into the instruction-following style. We propose three instruction types for finetuning ParroT models, including translation instruction, contrastive instruction, and error-guided instruction.
  arXiv  Detail & Related papers  (2023-04-05T13:12:00Z)
• Understanding Translationese in Cross-Lingual Summarization [106.69566000567598]
  Cross-lingual summarization (MS) aims at generating a concise summary in a different target language. To collect large-scale CLS data, existing datasets typically involve translation in their creation. In this paper, we first confirm that different approaches of constructing CLS datasets will lead to different degrees of translationese.
  arXiv  Detail & Related papers  (2022-12-14T13:41:49Z)

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.