北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2024, Vol. 47 ›› Issue (4): 20-28.

• 体系化人工智能专题 • 上一篇    下一篇

大语言模型时代的人工智能:技术内涵、行业应用与挑战

陈光,郭军   

  1.  北京邮电大学 人工智能学院

  • 收稿日期:2024-02-23 修回日期:2024-05-12 出版日期:2024-08-28 发布日期:2024-08-26
  • 通讯作者: 陈光 E-mail:chenguang@bupt.edu.cn

Artificial Intelligence in the Era of Large Language Models: Technical Significance, Industry Applications, and Challenges

CHEN Guang, GUO Jun   

  1. School of Artificial Intelligence, Beijing University of Posts and Telecommunications
  • Received:2024-02-23 Revised:2024-05-12 Online:2024-08-28 Published:2024-08-26
  • Contact: Guang Chen E-mail:chenguang@bupt.edu.cn

摘要: 大语言模型(LLM)的出现标志着人工智能 LLM 时代的来临。基于海量数据集的预训练,LLM 展现出卓越的适应性和创造力,正在成为推动社会发展的关键驱动力,并将在体系化人工智能中扮演重要角色。鉴于既有综述在分析 LLM 面临的挑战、关键属性、工程实现等方面的不足,笔者从技术内涵、行业应用和主要挑战三个维度重新构建探讨框架。重点阐述了 LLM 在系统架构、训练策略、模型规模、压缩、多模态融合、提示与规划等技术层面的内涵,以及在教育、科研、医疗、金融、司法等领域的应用前景。同时,讨论了 LLM 可信性、可控性与安全性的研究现状,以及 LLM 在技术和社会层面所面临的双重挑战,展望了 LLM 在体系化人工智能中的角色定位和研究方向的契合点,以期为 LLM 的研究与应用提供新的视角和思路。

关键词: 大型语言模型, 多媒态模型, 可信性, 可控性, 体系化人工智能

Abstract: The emergence of ChatGPT marks the advent of the era of artificial intelligence powered by large language models ( LLM ). Based on large-scale datasets for pre-training, LLMs demonstrate exceptional adaptability and creativity, becoming a critical driving force in advancing society and playing a significant role in systematic artificial intelligence. Given the limitations of existent reviews in analyzing the challenges faced by LLMs, their key attributes, and engineering implementation aspects. The framework is rediscussed and reconstructed from three dimensions: technical connotations, industry applications, and major challenges. The focus is on elucidating the connotation on the level of technical aspects of LLMs, including system architecture, training strategies, model scale, compression, multimodal fusion, prompting, and planning. It also explores the application prospects in various fields such as education, scientific research, healthcare, finance, and justice. Additionally, the discussion covers the current state of research on the reliability, controllability, and security of LLMs, as well as the dual challenges LLMs face on both technical and societal levels. It envisions the role of LLMs in systematic artificial intelligence and identifies alignment points in research directions, aiming to provide new perspectives and ideas for the research and application of LLMs.

Key words: large language models, multimodal models, trustworthiness, controllability, systematic artificial intelligence

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