Summary of Unsupervised Data Validation Methods For Efficient Model Training, by Yurii Paniv
Unsupervised Data Validation Methods for Efficient Model Training
by Yurii Paniv
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This abstract discusses the difficulties and potential solutions in developing machine learning systems for low-resource languages. Despite advances in natural language processing (NLP), text-to-speech (TTS), speech-to-text (STT), and vision-language models (VLM), these state-of-the-art models heavily rely on large datasets, which are often unavailable for low-resource languages. The authors explore 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 highlights both advancements and limitations in data augmentation, multilingual transfer learning, synthetic data generation, and data selection techniques. The paper identifies several open research questions, 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 authors aim to make advanced machine learning models more accessible for low-resource languages, enhancing their utility and impact across various sectors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machines learn new things using words from low- resource languages. Right now, most machines rely on big datasets, but those aren’t always available for low-resource languages. The researchers explore ways to get the right data and make it accessible to train models. They look at what’s currently working and what isn’t in areas like data augmentation, using similar languages to transfer knowledge, generating fake data, and choosing the right data. They also identify some open questions that need answering to make machines smarter for low-resource languages. |
Keywords
» Artificial intelligence » Data augmentation » Machine learning » Natural language processing » Nlp » Synthetic data » Transfer learning
