New Translation Model Lius Improves Low-Resource Language Performance Using Continual Instruction Tuning
Researchers have developed Lius, a fine-tuned large language model designed to improve translation for Kupang Malay, a low-resource language. The model uses Continual Instruction Tuning (CIT), which leverages bilingual dictionary features to enable iterative instruction-based training. The approach demonstrates 4-6 point improvements over standard instruction-tuned models and 10-13 point gains over existing Neural Machine Translation and Multilingual LLM baselines.
A new machine translation model called Lius addresses a persistent challenge in natural language processing: the performance degradation of large language models when handling low-resource languages like Kupang Malay. The researchers developed an approach that fine-tunes LLMs by designing instructions that explicitly leverage lexical and semantic features from bilingual dictionaries, combined with a novel training paradigm called Continual Instruction Tuning (CIT). This iterative instruction-based training method enables the model to improve through multiple rounds of refinement. Experimental results show Lius outperforms standard instruction-tuned models by 4-6 points on several evaluation metrics and substantially surpasses both traditional Neural Machine Translation systems and existing Multilingual LLM models by 10-13 points. The findings suggest this approach could reduce dependence on large-scale parallel data, which is often unavailable for low-resource languages.
What's missing
The paper does not specify which evaluation metrics were used (BLEU, METEOR, chrF, etc.), the size of the training dataset, or details about the bilingual dictionary employed. The generalizability of the approach to other low-resource languages remains unclear from the abstract.
What different sources said
- arXiv cs.CLCenter
Lius: Translation Model Based Instructional Lingustic Using Continual Instruction Tuning In Kupang Malay
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