Lius: Translation Model Based Instructional Lingustic Using Continual Instruction Tuning In Kupang Malay
arXiv:2606.11786v1 Announce Type: new Abstract: Large Language Models (LLMs) offer new potential for translation tasks but often experience performance degradation when handling low-resource languages. To address this limitation, we propose an approach for fine-tuning LLMs on a low-resource language, Kupang Malay. Our approach involves designing a set of instructions by leveraging explicit lexical and semantic features from a bilingual dictionary, and introducing Continual Instruction Tuning (CIT), a training paradigm that enables iterative instruction-based training. Experimental results demo
The proliferation of Large Language Models (LLMs) has amplified the need to address their limitations, particularly for low-resource languages, prompting innovative fine-tuning approaches.
This research provides a pathway to broaden AI accessibility and utility for diverse linguistic communities, fostering global inclusion and potentially unlocking new economic and social value in previously underserved regions.
The ability to effectively fine-tune LLMs for low-resource languages through methods like Continual Instruction Tuning fundamentally shifts how AI can be deployed and customized globally.
- · Southeast Asian language communities
- · AI researchers in natural language processing
- · Linguistic diversity efforts
- · Companies seeking to expand AI services globally
- · Monolingual AI solutions
- · Translation services reliant on traditional methods
Improved translation and communication tools become available for communities speaking low-resource languages.
Increased digital inclusion leads to new economic opportunities and educational access in these communities.
The development of regionally specific AI models challenges the dominance of global, Anglocentric AI frameworks, fostering a more multipolar AI landscape.
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