生成式人工智能在自然语言处理中的应用综述

doi:10.11896/jsjkx.241200156

摘要/Abstract

摘要： 随着大语言模型近年来的爆炸性发展,生成式人工智能(Artificial Intelligence Generated Content,AIGC)在自然语言处理(Natural Language Processing,NLP)中的应用成为人工智能领域的研究热点。区别于传统的分析与预测模型,生成式模型近年来在自然语言生成(Natural Language Generation,NLG)领域取得了显著进展,包括循环神经网络、长短时记忆网络、生成对抗网络、Transformer模型、变分自动编码器和扩散模型等。这些模型在自然语言领域的不同生成任务中都有着广泛的应用。得益于大语言模型的快速发展,生成式人工智能在问答系统、文本摘要、机器翻译、信息抽取等任务中取得了突出成果。然而,尽管生成式人工智能在自然语言处理中已取得巨大进展,但仍面临诸多挑战。未来,需要进一步优化模型的训练过程,提高其在多任务和跨领域应用中的泛化能力,同时解决生成内容的质量和安全性问题,以满足不断变化的新兴任务的需求。

关键词: 生成式人工智能, 自然语言处理, Transformer模型, 大语言模型, 跨领域应用

Abstract: With the explosive development of large language models in recent years,the applications of artificial intelligence ge-nerated content in natural language processing has become a research hotspot in the field of artificial intelligence.Unlike traditional analysis and prediction models,generative models have made significant progress in the field of natural language generation in recent years,including recurrent neural networks,long short-term memory networks,generative adversarial networks,Transformer models,variational autoencoders,and diffusion models.These models have found wide applications in various generation tasks within the natural language field.Owing to the rapid development of large language models,artificial intelligence generated content has achieved remarkable results in tasks such as question answering systems,text summarization,machine translation,information extraction,and other related tasks.However,despite the tremendous progress artificial intelligence generated content has made in natural language processing,many challenges still remain.In the future,it is necessary to further optimize the training process of related models,improve their generalization ability in multi-task and interdisciplinary applications,and address issues related to the quality and safety of generated content to meet the evolving demands of emerging tasks.

Key words: Artificial intelligence generated content, Natural language processing, Transformer model, Large language model, Interdisciplinary applications

中图分类号:

TP391

袁天浩, 王拥军, 王宝山, 王中原. 生成式人工智能在自然语言处理中的应用综述[J]. 计算机科学, 2025, 52(11A): 241200156-12. https://doi.org/10.11896/jsjkx.241200156

YUAN Tianhao, WANG Yongjun, WANG Baoshan, WANG Zhongyuan. Review of Artificial Intelligence Generated Content Applications in Natural Language Processing[J]. Computer Science, 2025, 52(11A): 241200156-12. https://doi.org/10.11896/jsjkx.241200156

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