| Challenge: | Low-resource languages such as those in the Finno-Ugric family are underrepresented in large language models. |
| Approach: | They propose to develop large language models for extremely low-resource languages . they focus on Vro, Livonian, and Komi, which are underrepresented . |
| Outcome: | The proposed models cover almost the entire cycle of creation, from data collection to instruction tuning and evaluation. |
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| Challenge: | Recent advances in AI can be attributed to the remarkable performance of Large Language Models (LLMs) success of LLMs depends on specific training techniques, such as instruction tuning and prompting . |
| Approach: | They explore the capabilities of Large Language Models (LLMs) in various tasks and languages . they also examine their performance, fine-tuning, instructions tuning, and close vs. open models . |
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High-quality Data-to-Text Generation for Severely Under-Resourced Languages with Out-of-the-box Large Language Models (2024.findings-eacl)
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| Challenge: | Pretrained large language models (LLMs) can bridge the performance gap for under-resourced languages by substantial margins, as measured by both automatic and human evaluations. |
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VEEF-Multi-LLM: Effective Vocabulary Expansion and Parameter Efficient Finetuning Towards Multilingual Large Language Models (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have a significant disadvantage for low-resource languages . VEEF-Multi-LLM-8B excels in multilingual instruction-following tasks . |
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Pipeline Analysis for Developing Instruct LLMs in Low-Resource Languages: A Case Study on Basque (2025.naacl-long)
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| Challenge: | Large language models are typically optimized for resource-rich languages like English . however, the proprietary nature of these models makes them impractical for many researchers and developers. |
| Approach: | They propose to develop large language models that can follow instructions in Basque . they focus on three key stages: pre-training, instruction tuning, and alignment with human preferences . |
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Data and Model Centric Approaches for Expansion of Large Language Models to New languages (2025.emnlp-tutorials)
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| Challenge: | Existing LLMs mainly support English alongside a handful of high resource languages . this leaves a major gap for most low-resource languages despite increasing pace of research . |
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A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languages (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are increasingly used to generate synthetic textual data for training smaller specialized models. |
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| Challenge: | Large language models (LLMs) have demonstrated impressive performance in machine translation, but struggle with unseen low-resource languages. |
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The Linguistic Connectivities Within Large Language Models (2025.findings-acl)
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Dan Wang, Boxi Cao, Ning Bian, Xuanang Chen, Yaojie Lu, Hongyu Lin, Jia Zheng, Le Sun, Shanshan Jiang, Bin Dong, Xianpei Han
| Challenge: | Recent studies have discovered notable disparities in their performance across different languages. |
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Instructing Large Language Models for Low-Resource Languages: A Systematic Study for Basque (2025.emnlp-main)
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Oscar Sainz, Naiara Perez, Julen Etxaniz, Joseba Fernandez de Landa, Itziar Aldabe, Iker García-Ferrero, Aimar Zabala, Ekhi Azurmendi, German Rigau, Eneko Agirre, Mikel Artetxe, Aitor Soroa
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| Outcome: | The proposed model outperforms base non-instructed models on Basque without Basque instructions. |
Scaling Low-Resource MT via Synthetic Data Generation with LLMs (2025.emnlp-main)
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Ona de Gibert, Joseph Attieh, Teemu Vahtola, Mikko Aulamo, Zihao Li, Raúl Vázquez, Tiancheng Hu, Jörg Tiedemann
| Challenge: | a recent study has shown that LLM-generated synthetic data can improve low-resource machine translation performance . traditional data augmentation techniques like back-translation preserve the human-written target and synthesize the other . |
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