Is LLM the Silver Bullet to Low-Resource Languages Machine Translation?

• URL: http://arxiv.org/abs/2503.24102v2
• Date: Thu, 05 Jun 2025 17:55:07 GMT
• Title: Is LLM the Silver Bullet to Low-Resource Languages Machine Translation?
• Authors: Yewei Song, Lujun Li, Cedric Lothritz, Saad Ezzini, Lama Sleem, Niccolo Gentile, Radu State, Tegawendé F. Bissyandé, Jacques Klein,
• Abstract summary: Low-Resource Languages (LRLs) present significant challenges in natural language processing due to their limited linguistic resources and underrepresentation in standard datasets.<n>Recent advances in Large Language Models (LLMs) and Neural Machine Translation have substantially improved translation capabilities for high-resource languages.<n>This paper systematically evaluates current LLMs in 200 languages and demonstrates their limitations in LRL translation capability.
• Score: 14.55410092719299
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Low-Resource Languages (LRLs) present significant challenges in natural language processing due to their limited linguistic resources and underrepresentation in standard datasets. While recent advances in Large Language Models (LLMs) and Neural Machine Translation have substantially improved translation capabilities for high-resource languages, performance disparities persist for LRLs, particularly impacting privacy-sensitive and resource-constrained scenarios. This paper systematically evaluates current LLMs in 200 languages using the FLORES-200 benchmark and demonstrates their limitations in LRL translation capability. We also explore alternative data sources, including news articles and bilingual dictionaries, and demonstrate how knowledge distillation from large pre-trained teacher models can significantly improve the performance of small LLMs on LRL translation tasks. For example, this approach increases EN->LB with the LLM-as-a-Judge score on the validation set from 0.36 to 0.89 for Llama-3.2-3B. Furthermore, we examine different fine-tuning configurations, providing practical insights on optimal data scale, training efficiency, and the preservation of generalization capabilities of models under study.

Related papers

• TALL -- A Trainable Architecture for Enhancing LLM Performance in Low-Resource Languages [13.416341692917676]
  This paper presents TALL (Trainable Architecture for Enhancing LLM Performance in Low-Resource Languages), which integrates an LLM with two bilingual translation models.<n>Our experiments on Hebrew demonstrate significant improvements over several baselines, including direct use, naive translation, and fine-tuning approaches.
  arXiv  Detail & Related papers  (2025-06-05T14:02:12Z)
• Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation [33.08089616645845]
  The advent of Large Language Models (LLMs) has significantly reshaped the landscape of machine translation (MT) We analyze techniques such as few-shot prompting, cross-lingual transfer, and parameter-efficient fine-tuning that enable effective adaptation to under-resourced settings. We discuss persistent challenges such as hallucinations, evaluation inconsistencies, and inherited biases while also evaluating emerging LLM-driven metrics for translation quality.
  arXiv  Detail & Related papers  (2025-04-02T17:26:40Z)
• Enhancing Code Generation for Low-Resource Languages: No Silver Bullet [55.39571645315926]
  Large Language Models (LLMs) rely on large and diverse datasets to learn syntax, semantics, and usage patterns of programming languages.<n>For low-resource languages, the limited availability of such data hampers the models' ability to generalize effectively.<n>We present an empirical study investigating the effectiveness of several approaches for boosting LLMs' performance on low-resource languages.
  arXiv  Detail & Related papers  (2025-01-31T12:23:28Z)
• Think Carefully and Check Again! Meta-Generation Unlocking LLMs for Low-Resource Cross-Lingual Summarization [108.6908427615402]
  Cross-lingual summarization ( CLS) aims to generate a summary for the source text in a different target language.<n>Currently, instruction-tuned large language models (LLMs) excel at various English tasks.<n>Recent studies have shown that LLMs' performance on CLS tasks remains unsatisfactory even with few-shot settings.
  arXiv  Detail & Related papers  (2024-10-26T00:39:44Z)
• Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation [62.202893186343935]
  We explore what it would take to adapt Large Language Models for low-resource languages. We show that parallel data is critical during both pre-training andSupervised Fine-Tuning (SFT) Our experiments with three LLMs across two low-resourced language groups reveal consistent trends, underscoring the generalizability of our findings.
  arXiv  Detail & Related papers  (2024-08-23T00:59:38Z)
• Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages [60.162717568496355]
  Large language models (LLMs) have been pre-trained on multilingual corpora. Their performance still lags behind in most languages compared to a few resource-rich languages.
  arXiv  Detail & Related papers  (2024-02-19T15:07:32Z)
• POMP: Probability-driven Meta-graph Prompter for LLMs in Low-resource Unsupervised Neural Machine Translation [32.76853731410492]
  Low-resource languages (LRLs) face challenges in supervised neural machine translation due to limited parallel data. We propose Probability-driven Meta-graph Prompter (POMP) to enhance Large Language Models' translation capabilities for LRLs. Our experiments show significant improvements in the translation quality of three LRLs.
  arXiv  Detail & Related papers  (2024-01-11T00:03:36Z)
• Zero-Shot Cross-Lingual Reranking with Large Language Models for Low-Resource Languages [51.301942056881146]
  We investigate how large language models (LLMs) function as rerankers in cross-lingual information retrieval systems for African languages. Our implementation covers English and four African languages (Hausa, Somali, Swahili, and Yoruba) We examine cross-lingual reranking with queries in English and passages in the African languages.
  arXiv  Detail & Related papers  (2023-12-26T18:38:54Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• 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)

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.