Aligning Large Language Models to Low-Resource Languages through LLM-Based Selective Translation: A Systematic Study

• URL: http://arxiv.org/abs/2507.14304v1
• Date: Fri, 18 Jul 2025 18:21:52 GMT
• Title: Aligning Large Language Models to Low-Resource Languages through LLM-Based Selective Translation: A Systematic Study
• Authors: Rakesh Paul, Anusha Kamath, Kanishk Singla, Raviraj Joshi, Utkarsh Vaidya, Sanjay Singh Chauhan, Niranjan Wartikar,
• Abstract summary: 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.
• Score: 1.0470286407954037
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multilingual large language models (LLMs) often demonstrate a performance gap between English and non-English languages, particularly in low-resource settings. Aligning these models to low-resource languages is essential yet challenging due to limited high-quality data. While English alignment datasets are readily available, curating equivalent data in other languages is expensive and time-consuming. A common workaround is to translate existing English alignment data; however, standard translation techniques often fail to preserve critical elements such as code, mathematical expressions, and structured formats like JSON. In this work, we investigate LLM-based selective translation, a technique that selectively translates only the translatable parts of a text while preserving non-translatable content and sentence structure. We conduct a systematic study to explore key questions around this approach, including its effectiveness compared to vanilla translation, the importance of filtering noisy outputs, and the benefits of mixing translated samples with original English data during alignment. Our experiments focus on the low-resource Indic language Hindi and compare translations generated by Google Cloud Translation (GCP) and Llama-3.1-405B. The results highlight the promise of selective translation as a practical and effective method for improving multilingual alignment in LLMs.

Related papers

• SenWiCh: Sense-Annotation of Low-Resource Languages for WiC using Hybrid Methods [1.2091341579150698]
  We release datasets of sentences containing polysemous words across ten low-resource languages.<n>To facilitate dataset creation, the paper presents a demonstrably beneficial semi-automatic annotation method.<n>Results highlight the importance of targeted dataset creation and evaluation for effective polysemy disambiguation.
  arXiv  Detail & Related papers  (2025-05-29T17:48:08Z)
• Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models [52.22235443948351]
  High-quality multilingual training data is essential for effectively pretraining large language models (LLMs)<n>Here, we introduce JQL, a systematic approach that efficiently curates diverse and high-quality multilingual data at scale.<n>JQL distills LLMs' annotation capabilities into lightweight annotators based on pretrained multilingual embeddings.
  arXiv  Detail & Related papers  (2025-05-28T11:06:54Z)
• 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)
• Understanding In-Context Machine Translation for Low-Resource Languages: A Case Study on Manchu [53.437954702561065]
  In-context machine translation (MT) with large language models (LLMs) is a promising approach for low-resource MT.<n>This study systematically investigates how each type of resource, e.g., dictionary, grammar book, and retrieved parallel examples, affect the translation performance.<n>Our results indicate that high-quality dictionaries and good parallel examples are very helpful, while grammars hardly help.
  arXiv  Detail & Related papers  (2025-02-17T14:53:49Z)
• A Data Selection Approach for Enhancing Low Resource Machine Translation Using Cross-Lingual Sentence Representations [0.4499833362998489]
  This study focuses on the case of English-Marathi language pairs, where existing datasets are notably noisy. To mitigate the impact of data quality issues, we propose a data filtering approach based on cross-lingual sentence representations. Results demonstrate a significant improvement in translation quality over the baseline post-filtering with IndicSBERT.
  arXiv  Detail & Related papers  (2024-09-04T13:49:45Z)
• In-Context Example Selection via Similarity Search Improves Low-Resource Machine Translation [20.704153242284114]
  We focus on machine translation (MT), a task that has been shown to benefit from in-context translation examples. No systematic studies have been published on how best to select examples, and mixed results have been reported on the usefulness of similarity-based selection. We find that sentence embedding similarity can improve MT, especially for low-resource language directions.
  arXiv  Detail & Related papers  (2024-08-01T09:07:32Z)
• 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)
• Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts [75.33019401706188]
  Large language models (LLMs) are known to effectively perform tasks by simply observing few exemplars. We propose to assemble synthetic exemplars from a diverse set of high-resource languages to prompt the LLMs to translate from any language into English. Our unsupervised prompting method performs on par with supervised few-shot learning in LLMs of different sizes for translations between English and 13 Indic and 21 African low-resource languages.
  arXiv  Detail & Related papers  (2023-06-20T08:27:47Z)
• Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation [105.41167108465085]
  Cross-lingual Machine Reading (CLMRC) remains a challenging problem due to the lack of large-scale datasets in low-source languages. We propose a novel augmentation approach named Language Branch Machine Reading (LBMRC) LBMRC trains multiple machine reading comprehension (MRC) models proficient in individual language. We devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages.
  arXiv  Detail & Related papers  (2020-10-27T13:12:17Z)
• FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding [85.29270319872597]
  We propose an enhanced fusion method that takes cross-lingual data as input for XLM finetuning. During inference, the model makes predictions based on the text input in the target language and its translation in the source language. To tackle this issue, we propose an additional KL-divergence self-teaching loss for model training, based on auto-generated soft pseudo-labels for translated text in the target language.
  arXiv  Detail & Related papers  (2020-09-10T22:42:15Z)

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.