Exploring Transfer Learning for Natural Language Processing Tasks
Abstract
Transfer learning has emerged as a transformative approach in natural language processing (NLP), enabling models to leverage pre-trained knowledge for downstream tasks. This paper investigates the performance of transfer learning techniques, including BERT, GPT, and T5, across various NLP tasks such as sentiment analysis, text summarization, and question answering. The study evaluates the impact of fine-tuning strategies, dataset size, and domain-specific adaptations on model performance. Experimental results reveal that transfer learning significantly reduces training time and enhances accuracy, particularly in low-resource settings. The paper concludes with a discussion on ethical considerations and the future of transfer learning in NLP.
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