Aug 2026· Technologies· Vol 14, pp. 518· 0 citations· 62 references
TL;DR
The findings reveal that model performance is highly dependent on resource availability: transformer-based NMT excels in moderate data settings, while LLMs demonstrate promising zero-shot and few-shot capabilities in extremely low-resource scenarios.
Abstract
The rapid evolution of AI-driven language technologies has inadvertently widened the gap between high-resource and marginalised languages. Despite significant progress in AI-driven translation for high-resource languages, low-resource languages remain underrepresented due to limited data, a lack of benchmarks, and evaluation challenges. This study presents a comprehensive systematic review of machine translation for low-resource languages, focusing on advances in neural machine translation (NMT) and large language models (LLMs) between 2017 and 2025. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 63 studies were selected from the 1696 articles in the Scopus, Web of Science, and Google Scholar databases. The review identifies five dominant methodological approaches: data augmentation, back-translation, transfer learning, pre-training, and parameter-efficient fine-tuning. The findings reveal that model performance is highly dependent on resource availability: transformer-based NMT excels in moderate data settings, while LLMs demonstrate promising zero-shot and few-shot capabilities in extremely low-resource scenarios. Hybrid NMT–LLM approaches emerge as a particularly effective paradigm. The study also highlights critical challenges, including the absence of standardised benchmarks, over-reliance on inadequate evaluation metrics such as Bilingual Evaluation Understudy (BLEU), limited human evaluation, and significant geographic and linguistic underrepresentation. Additionally, ethical concerns related to bias, cultural representation, and community engagement are increasingly relevant. The findings contribute to advancing inclusive and equitable AI-driven language technologies.
The findings show that a hybrid combination of TL and DA with a self-supervised objective is the most effective solution for extremely low-resource scenarios, capable of producing the highest translation quality and outperforming baseline models and traditional methods such as Statistical Machine Translation (SMT).
Nur Fikri Khuluq, Muhammad Naufal Muzhaffar, Shofwatul Uyun· Jurnal Sains, Nalar, dan Apl...· 0 citations
This systematic review examines recent progress in the pretraining and adaptation of LLMs for Low-Resource Languages (LRLs) and focuses on the ethics in AI practice, the development of corpora through communities, and interdisciplinary research collaboration among computational linguists, social scientists, and digital humanists.
Ismail Hossain, Mridul Banik, Fahmid Al Farid et al.· Computer Modeling in Enginee...· 0 citations
This paper introduces *TranslatePsy-AfriSLM*, a collection of open-source MT resources for 19 Sub-Saharan African languages, including curated parallel data, African-specialized synthetic data, and a family of fine-tuned SLMs.
Milan Gritta, Patrik Lambert, Jihye Back et al.· 0 citations
The findings show that manual transliteration consistently yields the best translation performance, while noisy automatic romanization reduces these gains, and that LLM-based translation can be competitive with, and in some settings outperform, fine-tuned NMT systems, although this advantage comes with lower interpretability.
A. Mansurova, Meruert Bekmukhamedova, Bekarys Baibolat et al.· Electronics· 0 citations
Large language models (LLMs) have achieved remarkable success in high-resource languages, yet their performance on Traditional Mongolian remains highly limited. A primary bottleneck is the absence of a systematic evaluation framework, which precludes quantitative comparison and obscures directions for model optimization. In this paper, we introduce TM-Bench, the first comprehensive benchmark for LLMs on Traditional Mongolian. TM-Bench adopts a hybrid construction strategy consisting of human-verified Translation-based Adaptation, Expert-Original Authoring, and Semi-automated Synthesis. It comprises 18,357 instances spanning five tasks across both natural language understanding and generation to evaluate models' reasoning, knowledge application, and linguistic proficiency. We conduct systematic evaluations across representative model families. The results show that on understanding tasks, model performance lags significantly behind high-resource languages, with only a few models performing slightly above the random baseline. For generation tasks, both automatic metrics and double-blind human evaluations reveal severe semantic collapse, failing to generate coherent text and often producing unreadable gibberish. These findings underscore the critical role of TM-Bench as a foundational infrastructure for evaluating LLMs in Traditional Mongolian and catalyzing future model optimization. Our benchmark and code are available at https://github.com/gao1948083886/TM-Bench.
Zhenjie Gao, Feilong Bao, Aruukhan Bai et al.· Annual International ACM SIG...· 0 citations
Mathematical reasoning has become a central task for evaluating and tuning reasoning Large Language Models (LLMs), yet existing benchmarks remain heavily biased toward high-resource languages, with English and Chinese dominating both pre-training corpora and evaluation suites. The recently released PolyMath (Wang et al., 2025) dataset represents a significant step forward, yet its coverage is still limited to 18 only high-resource languages. To address this gap, we introduce PluraMath, an extension of PolyMath to 18 additional {underrepresented languages spanning 6 language families -- ranging from mid-resource to extreme low-resource settings. We constructed the dataset through a human-curated pipeline, where native speakers thoroughly validated pre-computed translations. Using PluraMath, we then benchmark 27 reasoning LLMs across four model scales -- small, mid-size, large, and closed-source ensembles -- probing the multilingual mathematical reasoning capabilities of state-of-the-art models under diverse linguistic conditions. Our fine-grained analysis confirms a persistent gap in mathematical reasoning performance between high-resource and underrepresented languages, with stronger results largely associated with better instruction-following ability. We fully open-source our dataset, data acquisition pipeline, and evaluation framework, with the goal of lowering the barrier to multilingual benchmark development for underrepresented communities.
Daryna Dementieva, N. Babakov, Kathy Hammerl et al.· 0 citations
