Tackling Low-Resource NMT with Instruction-Tuned LL a MA : A Study on Kokborok and B odo (2025)

Abstract: This paper presents a new neural machine translation (NMT) system aimed at low-resource language pairs: English to Kokborok and English to Bodo. The framework leverages the LLaMA3-8B-Instruct model along with LoRA-based parameter-efficient fine-tuning. For translating into Kokborok, the model undergoes an initial continued pre-training phase on a dataset containing 75,000 Kokborok and 25,000 English monolingual sentences, followed by instruction-tuning. This tuning uses a reformulated version of WMT25 dataset, adapted to the Alpaca format to support instructional goals. In the Bodo translation, the model is pre-trained on a more extensive dataset of 350,000 Bodo and 125,000 English sentences, using a similar instruction-tuning approach. LoRA adapters are used to modify the large LLaMA3 model for these low-resource settings. Testing with the WMT25 test dataset reveals modest translation results, highlighting the difficulties in translating for low-resource languages. Translating English to Bodo, the model achieved a BLEU score of 4.38, a TER of 92.5, and a chrF score of 35.4. For English to Kokborok, it yielded scores of 5.59 in chrF, 105.4 in TER, and 0.17 in BLEU. These results underscore the intricacies of the task and highlight the critical need for further data collection, domain-specific adaptations, and improvements in model design to better support underrepresented languages.

## introduction despite significant advancements in neural machine translation (nmt) and the instruction-tuning of large language models (llms), the predominant focus of research and datasets remains on english and high-resource languages. instruction-tuning, a potent method to align llms with human preferences, has demonstrated efficacy primarily in contexts where english data are plentiful. conversely, low-resource languages frequently experi-ence a dearth of robust foundational models due to the scarcity of both monolingual and parallel corpora. in mitigating this limitation, recent studies investigate cross-lingual instruction-tuning by integrating translation-following demonstrations, which enable english-centric llms to generalize to novel languages. currently, evidence suggests that zero-shot cross-lingual transfer is achievable when instruction-tuning is meticulously calibrated, even in scenarios where only english instructions are employed, although factuality and fluency may be compromised in the target language. more comprehensively, multistage architectures, such as lingualift, further exemplify how codeswitched translation data and task alignment can enhance reasoning in low-resource environments without dependence on extensive multilingual corpora. in light of these insights, this paper introduces an innovative nmt framework targeting pairs of english to kokborok and english to bodo language, utilizing the llama3-8b-instruct model, fine-tuned with lora-based adapters for efficient parameter adaptation. the model is initially pre-trained on monolingual corpora of 75,000 kokborok and 25,000 english sentences for kokborok and 350,000 bodo and 125,000 english sentences for bodo, subsequently undergoing instructiontuning on wmt25-supplied english-target language parallel data reformatted into an alpaca style structure. this process aligns translation objectives with instruction follow-up behaviors within the llm. evaluation in wmt25 test sets reveals modest yet meaningful translation performance: for english to bodo, @xmath0.37, @xmath1.5, and @xmath2.4; for english to kokborok, @xmath3.59, @xmath4.4, and @xmath5.17. these findings highlight the considerable challenges inherent in low-resource machine translation and emphasize the crucial need for persistent data collection, domain adaptation, and alignment strategies. our key contributions are: ## linguistic background in this study, we focus on two low-resource indian languages, bodo and kokborok, both belonging to the tibeto-burman branch of the sino-tibetan language family. these languages are spoken primarily in the northeastern region of india and share several linguistic characteristics such as tonality, agglutinative morphology, and subject object verb (sov) word order. despite their socio-cultural and political significance, both languages remain significantly underrepresented in nlp research and lack sufficient digital resources for computational modeling. ## dataset preparation for this study, we collected a substantial amount of monolingual data in bodo, kokborok, and english. the dataset comprises approximately 350,000 sentences in bodo, 75,000 sentences in kokborok, and 450,000 sentences in english. all data was legally compliant for research use. for pre-training, we constructed two datasets, one for english to bodo and another for english to kokborok, by mixing monolingual data in the following ratios: we also prepared an instruction-tuning dataset in alpaca format. we collected wmt25 3 parallel training data for english to bodo and english to kokborok and converted it into alpaca style instruction response pairs. to enhance the robustness of the model, we maintained a variety of instruction types so that the model learned to follow multiple instruction formats. finally, we trained two lora-adapted llama models using these instruction-tuned datasets for @xmath6. ## results and analysis for en-trp, improvements are more modest but still consistent. the model reaches a bleu of 0.18 (vs. 0.0/0.1) and rouge-l of 0.0153 (vs. 0.0087). ter improves markedly to 105.49, compared to 155.1 and 158.9. meteor (0.0063) is slightly below few-shot (0.0090), and chrf (5.60) remains low, reflecting severe data sparsity and script mismatches. overall, our architecture substantially outperforms zero-shot and few-shot prompting across most metrics. gains are especially pronounced for bodo, while kokborok results highlight the extreme difficulty of this language pair but still show measurable progress over prompting-only baselines. ## conclusion existing literature supports the efficacy of curriculum based training, monolingual continued pre-training, and parameter efficient fine-tuning (peft) techniques for neural machine translation in low-resource settings. furthermore, preceding investigations have illustrated the advantages of instruction-following fine-tuning for enhancing cross-lingual generalization. nonetheless, certain gaps persist. specifically, there has been no research concentrating on the translation of kokborok and bodo through the application of instruction-following llms, such as llama3-instruct. additionally, there has been no exploration of monolingual continued pre-training followed by alpaca-style instruction-tuning facilitated by low-rank adaptation (lora). our research endeavors to bridge these gaps through the following approaches: leveraging monolingual corpora in kokborok and english to pre-train llama3-8b prior to instruction-tuning. implementing lora-based adapters to tailor the model for translation from english to kokborok and english to bodo. conducting evaluations using wmt25 parallel datasets to establish foundational metrics, such as a bleu score of 0.17 for kokborok. moreover, we underscore the existing methods' limitations and accentuate the necessity for further research in data augmentation and domain specific or architectural adaptation.