New Translation Model Lius Improves Low-Resource Language Performance Using Continual Instruction Tuning
Researchers from Universitas Gadjah Mada have developed Lius, a fine-tuned large language model designed to improve machine translation for Kupang Malay, a low-resource language. The model uses a technique called Continual Instruction Tuning (CIT), which leverages lexical and semantic features from a bilingual dictionary to iteratively train the model. The work addresses a significant gap in NLP research, where low-resource languages are frequently underserved by mainstream translation systems.
A master's thesis from Universitas Gadjah Mada's AI program introduces Lius, a translation model built on large language model (LLM) fine-tuning for Kupang Malay, a language with limited digital training data. The core innovation is Continual Instruction Tuning (CIT), a training paradigm that enables iterative, instruction-based learning using explicit lexical and semantic features drawn from a bilingual dictionary rather than large-scale parallel corpora. Experimental results show Lius outperforms standard instruction-tuned LLMs by 4–6 points and surpasses both Neural Machine Translation (NMT) systems and multilingual LLMs by 10–13 points across several evaluation metrics. The approach is notable for reducing dependence on large parallel datasets, which are scarce for low-resource languages. The paper was submitted to arXiv in June 2026 and has not yet undergone formal peer review.
What's missing
The size and composition of the test set, the base LLM used for fine-tuning, and whether the bilingual dictionary was manually curated or automatically constructed are not detailed in the abstract. As a preprint, the work has not been peer-reviewed, and independent replication has not been reported.
What different sources said
- arXiv cs.CLCenter
Lius: Translation Model Based Instructional Lingustic Using Continual Instruction Tuning In Kupang Malay
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