Harnessing Large Language Models for Transformative Applications in Natural Language Processing
Adalat Khan Fahim, Sami Bin Azad, Tanvir Rashid, Ebrahim Abdulraqeb Ahmed Albaidaei, Houssam Ighzali · European Journal of Theoretical and Applied Sciences · 2024
This review article highlights the paradigm shift brought in by LLMS in NLP, coupling their capabilities and applications with related challenges. Architectures like Transformers have given LLMs prominence. GPT, BERT, and PaLM brought tremendous improvement over tasks like text classification, machine translation, and conversational AI, demonstrating almost human-level accuracy in comprehension and generation. These models pave the way for applications in healthcare, finance, and multilingual customer service-disconnected domains made possible by their generalization and transfer learning capabilities. With the Adoption of LLMs, there is also a growing concern for high computational costs, data privacy, and inherent biases, which pose an ethical and environmental challenge. The paper concludes with a gaze into the future of NLP, emphasizing that responsible development practices should be followed to maximize the benefits of LLMs while decreasing their societal and environmental impact.