Progressive Perturbation with KTO for Enhanced Machine Translation of I ndian Languages (2025)

Abstract: This study addresses the critical challenge of data scarcity in machine translation for Indian languages, particularly given their morphological complexity and limited parallel data. We investigate an effective strategy to maximize the utility of existing data by generating negative samples from positive training instances using a progressive perturbation approach. This is used for aligning the model with preferential data using Kahneman-Tversky Optimization (KTO). Comparing it against traditional Supervised Fine-Tuning (SFT), we demonstrate how generating negative samples and leveraging KTO enhances data efficiency. By creating rejected samples through progressively perturbed translations from the available dataset, we fine-tune the Llama 3.1 Instruct 8B model using QLoRA across 16 language directions, including English, Hindi, Bangla, Tamil, Telugu, and Santali. Our results show that KTO-based preference alignment with progressive perturbation consistently outperforms SFT, achieving significant gains in translation quality with an average BLEU increase of 1.84 to 2.47 and CHRF increase of 2.85 to 4.01 compared to SFT for selected languages, while using the same positive training samples and under similar computational constraints. This highlights the potential of our negative sample generation strategy within KTO, especially in low resource scenarios.

## related work low resource machine translation (mt) remains a persistent challenge, motivating a variety of strategies to maximize data efficiency. early work demonstrated that careful structuring of training data can significantly impact convergence and overall translation quality. for example, @xcite introduced a competence-based curriculum that adapts the complexity of training examples to the model's evolving capabilities. in a similar vein, @xcite and @xcite showed that progressively increasing data complexity by ordering training examples from simple to complex can lead to faster convergence and improved performance in mt. in addition to curriculum learning, data augmentation techniques have been widely explored to overcome the scarcity of parallel corpora in lowresource settings. @xcite augmented training data using monolingual corpora from related high-resource languages, thereby enriching the available signal without the need for additional bilingual data. similarly, @xcite proposed a method that leverages bilingual word embeddings and transformer-based representations (e.g., bert @xcite ) to introduce new words and increase the presence of rare vocabulary items in the training corpus. while effective, these approaches typically require access to supplementary resources or complex augmentation pipelines. data quality also plays a critical role in mt, particularly when dealing with automatically generated or noisy datasets. to address this, @xcite developed a data selection method that uses cross-lingual sentence representations derived from a multilingual sbert model @xcite to filter out semantically mismatched sentence pairs. this filtering enhances the reliability of the training data but does not directly tackle the challenge of making optimal use of the available examples. multilingual transfer learning offers another avenue for improving low-resource mt by exploiting the inherent relatedness between languages. @xcite ) combined techniques such as unified transliteration and shared subword segmentation with pre-training across multiple languages to enhance transfer learning capabilities. although effective, such approaches generally require a joint training framework that spans multiple language pairs. in contrast to these paradigms, our work adopts a preference-based optimization strategy that directly maximizes the utility of existing data. instead of relying solely on positive examples or external augmentation, we generate informative negative samples through a progressive perturbation strategy. by systematically degrading high-quality translations, our approach creates rejected samples that force the model to learn fine-grained distinctions between accurate and flawed outputs. ## methodology we opted to carry out our experiments across six distinct languages divided into three categories as listed below, originating from three to four different language families and varying in resource availability. to address data scarcity in indian language translation, we compare zero-shot inference (baseline), sft, and kto. all experiments use the llamafactory toolkit foot_0 @xcite . ## fine-tuning and optimization we compare sft with kto, both applied to the llama 3.1 instruct 8b model. ## evaluation we evaluated the translation quality of the finetuned models using bleu foot_2 @xcite and chrf foot_3 @xcite on the dev and devtest splits of the flores-200 benchmark dataset foot_4 @xcite . we used the sacrebleu library for bleu and chrf calculation. the flores-200 dataset provided a comprehensive benchmark for evaluating machine translation across various language pairs. 6 results and discussion 6.1 overall performance comparison (sft vs. ## conclusion this study explores kto with progressive perturbation for indian language translation, demonstrating its superiority over sft in most cases and highlighting its potential to maximize the utility of existing data in resource-scarce scenarios. our method systematically degrades high-quality translations through controlled perturbations, generating a spectrum of negative examples ranging from overtly erroneous to subtly flawed outputs. these negative samples provide a rich training signal, helping the model distinguish between accurate and error-prone translations, thereby enabling efficient learning from limited data. notably, it2-generated translations were more effective than gold-standard translations as preferred completions, raising questions about the reliability of the gold data in the bpcc dataset. however, kto was less effective in extremely low-resource cases like santali, where sft outperformed it, suggesting that kto's effectiveness depends on the model's initial proficiency in a given language.