| Challenge: | Setswana is a Bantu language spoken by an estimated five to ten million people worldwide. |
| Approach: | They propose to make setswana-based models available for the first time using data available from setswa and setswegian databases. |
| Outcome: | The proposed models outperform GPT-4o and Gemini 1.5 Pro on English-Setswana translation tasks and achieve state-of-the-art performance on Setswanan reasoning tasks. |
Similar Papers
NGLUEni: Benchmarking and Adapting Pretrained Language Models for Nguni Languages (2024.lrec-main)
Copied to clipboard
| Challenge: | Nguni languages have over 20 million home language speakers in South Africa . there has been considerable growth in the datasets for these languages, but no analysis of the performance of NLP models for these language has been reported across languages and tasks. |
| Approach: | They compile publicly available datasets for natural language understanding and generation, spanning 6 tasks and 11 datasets. |
| Outcome: | The proposed models outperform existing models and large-scale adapted models on cross-lingual transfer and machine translation. |
FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data (2024.emnlp-industry)
Copied to clipboard
Haoran Sun, Renren Jin, Shaoyang Xu, Leiyu Pan, null Supryadi, Menglong Cui, Jiangcun Du, Yikun Lei, Lei Yang, Ling Shi, Juesi Xiao, Shaolin Zhu, Deyi Xiong
| Challenge: | Large language models exhibit significant performance discrepancies between high- and low-resource languages. |
| Approach: | They present an open-source multilingual LLM with 8 billion parameters and a multilingual instruction dataset. |
| Outcome: | The proposed model achieves consistent multilingual representations across languages. |
BERTifying Sinhala - A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification (2022.lrec-1)
Copied to clipboard
| Challenge: | Large-scale monolingual pre-trained language models have shown promising results for high-resource as well as lowresource languages, especially for text classification. |
| Approach: | They provide a set of recommendations for using pre-trained models for Sinhala text classification and introduce new annotated datasets useful for future research. |
| Outcome: | The proposed models are far superior to existing models for Sinhala and set a strong baseline for text classification when fine-tuned. |
Paramanu: Compact and Competitive Monolingual Language Models for Low-Resource Morphologically Rich Indian Languages (2026.acl-long)
Copied to clipboard
| Challenge: | Multilingual large language models are expensive to pretrain and suffer from imbalances across languages and datasets. |
| Approach: | They propose a family of Indian language-only autoregressive language models trained on open-source language-specific data for the five most spoken Indian languages. |
| Outcome: | The proposed model outperforms most larger models up to 8B across all five languages. |
Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study (2025.naacl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have shown continuously improving multilingual capabilities. |
| Approach: | They evaluate the ability of open LLMs to handle multilingual machine translation tasks using a parallel-first monolingual-second data mixing strategy. |
| Outcome: | The proposed model outperforms state-of-the-art models and achieves competitive performance with Google Translate and GPT-4-turbo. |
Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages (2023.acl-long)
Copied to clipboard
Ayyoob ImaniGooghari, Peiqin Lin, Amir Hossein Kargaran, Silvia Severini, Masoud Jalili Sabet, Nora Kassner, Chunlan Ma, Helmut Schmid, André Martins, François Yvon, Hinrich Schütze
| Challenge: | Lack of LLMs supporting low-resource languages is a serious impediment to bringing NLP to all of the world. |
| Approach: | They create a model that scales LLMs horizontally and a corpus that covers 511 low-resource languages. |
| Outcome: | The proposed model improves on five diverse tasks across low- and high-resource languages. |
Sinhala Encoder-only Language Models and Evaluation (2025.acl-long)
Copied to clipboard
Tharindu Ranasinghe, Hansi Hettiarachchi, Nadeesha Chathurangi Naradde Vidana Pathirana, Damith Premasiri, Lasitha Uyangodage, Isuri Nanomi Arachchige, Alistair Plum, Paul Rayson, Ruslan Mitkov
| Challenge: | Recent advances in language models (LMs) have produced excellent results in many NLP tasks, but their effectiveness is highly dependent on available pre-training resources. |
| Approach: | They propose to collect the largest monolingual corpus for Sinhala and compile a benchmark and evaluate LMs on it. |
| Outcome: | The proposed language models outperform the popular multilingual LMs in downstream NLP tasks. |
XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models (2023.emnlp-main)
Copied to clipboard
Davis Liang, Hila Gonen, Yuning Mao, Rui Hou, Naman Goyal, Marjan Ghazvininejad, Luke Zettlemoyer, Madian Khabsa
| Challenge: | Large multilingual models rely on a single vocabulary shared across 100+ languages . this vocabulary bottleneck limits the representational capabilities of multilingual model XLM-R . |
| Approach: | They propose a new approach for scaling to large multilingual vocabularies by de-emphasizing token sharing between languages with little lexical overlap and assigning vocabulary capacity to achieve sufficient coverage for each individual language. |
| Outcome: | The proposed model outperforms XLM-R on all language tasks and is particularly effective on low-resource tasks. |
LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) exhibit exceptional translation capabilities in high-resource language tasks, yet their effectiveness in low-resourced languages is suboptimal. |
| Approach: | They conduct extensive multilingual continual pre-training on the LLaMA series models and develop LLiMAX for translation support across more than 100 languages. |
| Outcome: | The proposed model achieves higher translation performance than existing open-source models and performs on-par with specialized translation model on the Flores-101 benchmark. |
AfriInstruct: Instruction Tuning of African Languages for Diverse Tasks (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) for African languages perform worse compared to high-resource languages. |
| Approach: | They propose a model that specializes in instruction-tuning of multiple African languages covering various tasks. |
| Outcome: | The proposed model outperforms GPT-3.5-Turbo and other models of similar size in multiple tasks. |