Deep learning approaches for improving question answering systems in hepatocellular carcinoma research
Shuning Huo, Yafei Xiang, Hanyi Yu, Mengran Zhu, Yulu Gong
Abstract
In recent years, advancements in natural language processing (NLP) have been fueled by deep learning techniques, particularly through the utilization of powerful computing resources like GPUs and TPUs. Models like BERT and GPT-3, which have been trained on vast amounts of data, have revolutionised language understanding and generation. Pretrained models serve as robust bases for various tasks, including semantic understanding, intelligent writing, and reasoning, paving the way for a more generalized form of artificial intelligence. Natural language processing (NLP), avital application of AI, aims to bridge the gap between humans and computers through natural language interaction. This paper explores the current landscape and future prospects of large-scale model-based NLP, with a focus on question-answering systems within this domain. Practical cases and developments in artificial intelligence-driven question-answering systems are analyzed to foster further exploration and research in the realm of large-scale NLP.