Unsupervised Data Validation Methods for Efficient Model Training

TL;DR

The paper addresses the data scarcity problem in low-resource languages across NLP, TTS, STT, and VLMs, where large datasets are impractical to obtain. It surveys data scarcity solutions—data augmentation, multilingual transfer, synthetic data generation, data selection, and data validation—and discusses their limitations and cross-modal implications. It highlights promising directions like synthetic data with self-improvement loops and cross-modal alignment to boost performance under limited data, as well as the potential of multimodal architectures and improved tokenization to facilitate transfer. The work identifies open research questions, including formal resource definitions, datapoint validity, benchmarking in low-resource settings, and unsupervised multimodal data construction, with the aim of reducing data requirements while preserving model quality and expanding access to advanced models for diverse languages and applications.

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.