ADVANCES IN TRANSFER LEARNING FOR NATURAL LANGUAGE PROCESSING
Keywords:
Transfer Learning; Natural Language Processing (NLP); Pre-trained Language Models; BERT; GPT; T5; Transformer Architecture; Deep Learning; Fine-Tuning; Few-Shot Learning; Zero-Shot Learning; Multilingual NLP; Domain-Specific Models; Text Classification; Sentiment Analysis; Question Answering; Summarization; Conversational AI; Model Interpretability; Data Bias; Low-Resource Languages; Multimodal Integration; Efficient NLP; Green AI; Sustainable Artificial Intelligence.Abstract
Transfer learning has emerged as a transformative approach in Natural Language Processing (NLP), revolutionizing how language models acquire, transfer, and apply knowledge across diverse linguistic tasks. By leveraging large-scale pre-trained models such as BERT, GPT, and T5, transfer learning has significantly reduced the dependence on extensive labeled datasets, improving efficiency and generalization across applications including text classification, sentiment analysis, translation, summarization, and conversational AI. This review highlights the evolution of transfer learning from early word embeddings to transformer-based architectures, emphasizing their adaptability in multilingual and domain-specific contexts. Despite remarkable success, challenges persist regarding computational cost, interpretability, data bias, and limited support for low-resource languages. The paper further explores future directions such as few-shot and zero-shot learning, efficient fine-tuning, multimodal integration, and sustainable “Green AI” approaches to enhance scalability and environmental efficiency. Overall, transfer learning stands at the forefront of NLP innovation, offering a robust pathway toward more generalizable, interpretable, and energy-efficient intelligent language systems.
