Compensating for Data with Reasoning: Low-Resource Machine Translation with LLMs

• URL: http://arxiv.org/abs/2505.22293v1
• Date: Wed, 28 May 2025 12:29:05 GMT
• Title: Compensating for Data with Reasoning: Low-Resource Machine Translation with LLMs
• Authors: Samuel Frontull, Thomas Ströhle,
• Abstract summary: Fragment-Shot Prompting is a novel in-context learning method that segments input and retrieves translation examples based on syntactic coverage.<n> Pivoted Fragment-Shot is an extension that enables translation without direct parallel data.<n>We evaluate these methods using GPT-3.5, GPT-4o, o1-mini, LLaMA-3.3, and DeepSeek-R1 for translation between Italian and two Ladin variants.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated strong capabilities in multilingual machine translation, sometimes even outperforming traditional neural systems. However, previous research has highlighted the challenges of using LLMs, particularly with prompt engineering, for low-resource languages. In this work, we introduce Fragment-Shot Prompting, a novel in-context learning method that segments input and retrieves translation examples based on syntactic coverage, along with Pivoted Fragment-Shot, an extension that enables translation without direct parallel data. We evaluate these methods using GPT-3.5, GPT-4o, o1-mini, LLaMA-3.3, and DeepSeek-R1 for translation between Italian and two Ladin variants, revealing three key findings: (1) Fragment-Shot Prompting is effective for translating into and between the studied low-resource languages, with syntactic coverage positively correlating with translation quality; (2) Models with stronger reasoning abilities make more effective use of retrieved knowledge, generally produce better translations, and enable Pivoted Fragment-Shot to significantly improve translation quality between the Ladin variants; and (3) prompt engineering offers limited, if any, improvements when translating from a low-resource to a high-resource language, where zero-shot prompting already yields satisfactory results. We publicly release our code and the retrieval corpora.

Related papers

• Aligning Large Language Models to Low-Resource Languages through LLM-Based Selective Translation: A Systematic Study [1.0470286407954037]
  selective translation is a technique that translates only the translatable parts of a text while preserving non-translatable content and sentence structure.<n>Our experiments focus on the low-resource Indic language Hindi and compare translations generated by Google Cloud Translation (GCP) and Llama-3.1-405B.
  arXiv  Detail & Related papers  (2025-07-18T18:21:52Z)
• Trans-Zero: Self-Play Incentivizes Large Language Models for Multilingual Translation Without Parallel Data [64.4458540273004]
  We propose a self-play framework that leverages only monolingual data and the intrinsic multilingual knowledge of Large Language Models (LLMs)<n>Experiments demonstrate that this approach not only matches the performance of models trained on large-scale parallel data but also excels in non-English translation directions.
  arXiv  Detail & Related papers  (2025-04-20T16:20:30Z)
• 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)
• Learning-From-Mistakes Prompting for Indigenous Language Translation [3.7790255156708397]
  This paper presents techniques to improve extremely low-resourced indigenous language translations. Our approaches are grounded in the use of a datastore consisting of a limited number of parallel translation examples. We harness the potential of LLMs and in-context learning techniques in such a setting for using LLMs as universal translators.
  arXiv  Detail & Related papers  (2024-07-18T09:41:20Z)
• Shortcomings of LLMs for Low-Resource Translation: Retrieval and Understanding are Both the Problem [4.830018386227]
  This work investigates the in-context learning abilities of pretrained large language models (LLMs) when instructed to translate text from a low-resource language into a high-resource language as part of an automated machine translation pipeline. We conduct a set of experiments translating Southern Quechua to Spanish and examine the informativity of various types of context retrieved from a constrained database of digitized pedagogical materials and parallel corpora.
  arXiv  Detail & Related papers  (2024-06-21T20:02:22Z)
• 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)
• Exploring Human-Like Translation Strategy with Large Language Models [93.49333173279508]
  Large language models (LLMs) have demonstrated impressive capabilities in general scenarios. This work proposes the MAPS framework, which stands for Multi-Aspect Prompting and Selection. We employ a selection mechanism based on quality estimation to filter out noisy and unhelpful knowledge.
  arXiv  Detail & Related papers  (2023-05-06T19:03:12Z)
• DEEP: DEnoising Entity Pre-training for Neural Machine Translation [123.6686940355937]
  It has been shown that machine translation models usually generate poor translations for named entities that are infrequent in the training corpus. We propose DEEP, a DEnoising Entity Pre-training method that leverages large amounts of monolingual data and a knowledge base to improve named entity translation accuracy within sentences.
  arXiv  Detail & Related papers  (2021-11-14T17:28:09Z)
• Modelling Latent Translations for Cross-Lingual Transfer [47.61502999819699]
  We propose a new technique that integrates both steps of the traditional pipeline (translation and classification) into a single model. We evaluate our novel latent translation-based model on a series of multilingual NLU tasks. We report gains for both zero-shot and few-shot learning setups, up to 2.7 accuracy points on average.
  arXiv  Detail & Related papers  (2021-07-23T17:11:27Z)
• Exploiting Neural Query Translation into Cross Lingual Information Retrieval [49.167049709403166]
  Existing CLIR systems mainly exploit statistical-based machine translation (SMT) rather than the advanced neural machine translation (NMT) We propose a novel data augmentation method that extracts query translation pairs according to user clickthrough data. Experimental results reveal that the proposed approach yields better retrieval quality than strong baselines.
  arXiv  Detail & Related papers  (2020-10-26T15:28:19Z)
• Knowledge Distillation for Multilingual Unsupervised Neural Machine Translation [61.88012735215636]
  Unsupervised neural machine translation (UNMT) has recently achieved remarkable results for several language pairs. UNMT can only translate between a single language pair and cannot produce translation results for multiple language pairs at the same time. In this paper, we empirically introduce a simple method to translate between thirteen languages using a single encoder and a single decoder.
  arXiv  Detail & Related papers  (2020-04-21T17:26:16Z)

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.