EXPLORING UNSUPERVISED LEARNING METHODS IN NATURAL LANGUAGE PROCESSING

This paper reviews advances in unsupervised learning in natural language processing (NLP). Areas such as large language models like GPT-3 and T5, variational learning methods like SimCSE and BYOL, and applications of unsupervised learning in multilingual NLP such as XLM- R and mBERT are discussed. This paper focuses on how these models can use unsupervised data to improve the performance of tasks including machine translation, text generation, and classification. It also discusses practical applications of these techniques in tasks including text classification and clustering, and finally, the evaluation metrics used to assess the performance of models like SuperGLUE and SQuAD. It concludes that unsupervised learning has significant potential in the field of natural language processing, providing powerful tools that enable language models to understand human language more deeply and accurately.