Adapting Large Language Models to a Low-Resource Agglutinative Language: A Comparative Study of LoRA and QLoRA for Bashkir
arXiv:2605.04948v2 Announce Type: replace Abstract: This paper presents a comparative study of parameter-efficient fine-tuning (PEFT) methods, including LoRA and QLoRA, applied to the task of adapting large language models to the Bashkir language, a low-resource agglutinative language of the Turkic family. Experimental evaluation is conducted on a Bashkir text corpus of 71k documents (46.9M tokens) using models of various architectures: DistilGPT2, GPT-2 (base, medium), Phi-2, Qwen2.5-7B, DeepSeek-7B, and Mistral-7B. To improve the reliability of results, each configuration was trained with th
The proliferation of open-source LLMs and PEFT methods allows for more accessible and efficient adaptation to diverse languages, pushing research into low-resource contexts.
This research demonstrates progress in making advanced AI models accessible and performant for languages with scarce data, which is crucial for digital inclusivity and counteracting AI language homogenization.
The ability to effectively adapt LLMs to low-resource languages using parameter-efficient fine-tuning means more diverse linguistic communities can benefit from AI advancements without needing massive compute.
- · Speakers of low-resource languages
- · Linguistic diversity efforts
- · Open-source AI research
- · AI developers focused on PEFT
- · Monolingual AI solutions
- · AI models requiring vast proprietary datasets for each language
Improved AI tooling and applications become available for previously underserved linguistic groups.
This could foster new domestic AI ecosystems developing around specific cultural and linguistic needs, reducing dependency on a few dominant languages.
It might accelerate the development of sovereign AI capabilities in nations with unique linguistic heritage, as they can adapt existing models more affordably.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL