Unsupervised Data Validation Methods for Efficient Model Training

• URL: http://arxiv.org/abs/2410.07880v1
• Date: Thu, 10 Oct 2024 13:00:53 GMT
• Title: Unsupervised Data Validation Methods for Efficient Model Training
• Authors: Yurii Paniv,
• Abstract summary: 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. This research explores key areas such as defining "quality data," developing methods for generating appropriate data and enhancing accessibility to model training.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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