自然语言处理是实现人机交互的关键步骤，而汉语自然语言处理(Chinese natural language processing，CNLP)是其中的重要组成部分。随着大模型技术的发展，CNLP进入了一个新的阶段，这些汉语大模型具备更强的泛化能力和更快的任务适应性。然而，相较于英语大模型，汉语大模型在逻辑推理和文本理解能力方面仍存在不足。介绍了图神经网络在特定CNLP任务中的优势，进行了量子机器学习在CNLP发展潜力的调查。总结了大模型的基本原理和技术架构，详细整理了大模型评测任务的典型数据集和模型评价指标，评估比较了当前主流的大模型在CNLP任务中的效果。分析了当前CNLP存在的挑战，并对CNLP任务的未来研究方向进行了展望，希望能帮助解决当前CNLP存在的挑战，同时为新方法的提出提供了一定的参考。
Research and Exploration on Chinese Natural Language Processing in Era of Large Language Models
Natural language processing is a key step in realizing human-computer interaction,and Chinese natural lan-guage processing(CNLP)is an important part of it.With the development of big model technology,CNLP has entered a new stage,and these Chinese big models have stronger generalization ability and faster task adaptability.However,com-pared to English big models,Chinese big models are still deficient in logical reasoning and text comprehension ability.The advantages of graph neural networks in specific CNLP tasks are introduced,and a survey on the development poten-tial of quantum machine learning in CNLP is conducted.The basic principles and technical architectures of big models are summarized,the typical datasets and model evaluation indexes for big model evaluation tasks are organized in detail,and the effects of current mainstream big models in CNLP tasks are evaluated and compared.The current challenges of CNLP are analyzed,and the future research direction of CNLP task is outlooked,which is hoped to help solve the current chal-lenges of CNLP,and provide some references for the proposal of new methods.
Chinese natural language processinggraph neural networksquantum machine learningChinese large lan-guage models
万方数据
维普
