Deep Learning Approaches for Improving Question Answering Systems in Hepatocellular Carcinoma Research
<|reference_start|>Deep Learning Approaches for Improving Question Answering Systems in Hepatocellular Carcinoma Research: 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.<|reference_end|>
@article{huo2024deep,
    title={Deep Learning Approaches for Improving Question Answering Systems in
  Hepatocellular Carcinoma Research},
    author={Shuning Huo, Yafei Xiang, Hanyi Yu, Mengran Zhu, Yulu Gong},
    journal={arXiv preprint arXiv:2402.16038},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16038},
    primaryClass={cs.CL cs.AI cs.LG}
}