Deep Learning Approaches for Improving Question Answering Systems in
  Hepatocellular Carcinoma Research
Shuning Huo Yafei Xiang Hanyi Yu Mengran Zhu Yulu Gong
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 such as BERT and GPT-3,
trained on vast amounts of data, have revolutionized language understanding and
generation. These pre-trained 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. NLP, as a vital
application of AI, aims to bridge the gap between humans and computers through
natural language interaction. This paper delves into the current landscape and
future prospects of large-scale model-based NLP, focusing on the
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.