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问答ChatGPT之后:超大预训练模型的机遇和挑战
卢经纬; 郭超; 戴星原; 缪青海; 王兴霞; 杨静; 王飞跃
Source Publication自动化学报
ISSN0254-4156
2023
Volume49Issue:4Pages:705-717
Abstract超大预训练模型(Pre-trained model, PTM)是人工智能领域近年来迅速崛起的研究方向,在自然语言处理(Natural language processing, NLP)和计算机视觉等多种任务中达到了有史以来的最佳性能,促进了人工智能生成内容(Artificial intelligence-generated content, AIGC)的发展和落地. ChatGPT作为当下最火热的PTM,更是以优异的表现获得各界的广泛关注.本文围绕ChatGPT展开.首先概括PTM的基本思想并对其发展历程进行梳理;接着,详细探讨ChatGPT的技术细节,并以平行智能的视角阐述ChatGPT;最后,从技术、范式以及应用等多个方面对PTM的发展趋势进行展望.
Keyword预训练模型 ChatGPT Transformer 人工智能生成内容 平行智能 社会化大闭环
DOI10.16383/j.aas.c230107
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/56161
Collection学术期刊_自动化学报
Recommended Citation
GB/T 7714
卢经纬,郭超,戴星原,等. 问答ChatGPT之后:超大预训练模型的机遇和挑战[J]. 自动化学报,2023,49(4):705-717.
APA 卢经纬.,郭超.,戴星原.,缪青海.,王兴霞.,...&王飞跃.(2023).问答ChatGPT之后:超大预训练模型的机遇和挑战.自动化学报,49(4),705-717.
MLA 卢经纬,et al."问答ChatGPT之后:超大预训练模型的机遇和挑战".自动化学报 49.4(2023):705-717.
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