Unsupervised Data Validation Methods for Efficient Model Training
<|reference_start|>Unsupervised Data Validation Methods for Efficient Model Training: This paper investigates the challenges and potential solutions for improving machine learning systems for low-resource languages. State-of-the-art models in natural language processing (NLP), text-to-speech (TTS), speech-to-text (STT), and vision-language models (VLM) rely heavily on large datasets, which are often unavailable for low-resource languages. This research explores key areas such as defining "quality data," developing methods for generating appropriate data and enhancing accessibility to model training. A comprehensive review of current methodologies, including data augmentation, multilingual transfer learning, synthetic data generation, and data selection techniques, highlights both advancements and limitations. Several open research questions are identified, providing a framework for future studies aimed at optimizing data utilization, reducing the required data quantity, and maintaining high-quality model performance. By addressing these challenges, the paper aims to make advanced machine learning models more accessible for low-resource languages, enhancing their utility and impact across various sectors.<|reference_end|>
@article{paniv2024unsupervised,
    title={Unsupervised Data Validation Methods for Efficient Model Training},
    author={Yurii Paniv},
    journal={arXiv preprint arXiv:2410.07880},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07880},
    primaryClass={cs.CL cs.LG}
}