大模型时代下的汉语自然语言处理研究与探索  

Research and Exploration on Chinese Natural Language Processing in Era of Large Language Models

作　　者：黄施洋 奚雪峰 崔志明[1,2,3] HUANG Shiyang;XI Xuefeng;CUI Zhiming(School of Electronic and Information Engineering,Suzhou University of Science and Technology,Suzhou,Jiangsu 215000,China;Suzhou Key Laboratory of Virtual Reality Intelligent Interaction and Application Technology,Suzhou,Jiangsu 215000,China;Smart City Research Institute,Suzhou University of Science and Technology,Suzhou,Jiangsu 215000,China)

机构地区：[1]苏州科技大学电子与信息工程学院,江苏苏州215000 [2]苏州市虚拟现实智能交互及应用技术重点实验室,江苏苏州215000 [3]苏州科技大学智慧城市研究院,江苏苏州215000

出　　处：《计算机工程与应用》2025年第1期80-97,共18页Computer Engineering and Applications

基　　金：国家自然科学基金(62176175);江苏省“六大人才高峰”高层次人才项目(XYDXX-086);苏州市科技计划项目(SGC2021078)。

摘　　要：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。Natural language processing is a key step in realizing human-computer interaction,and Chinese natural language 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,compared 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 potential 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 challenges of CNLP,and provide some references for the proposal of new methods.

关 键 词：汉语自然语言处理 图神经网络 量子机器学习 汉语大模型 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]