The Journey of Language Models in Understanding Natural Language

被引：0

作者：

Liu, Yuanrui ^{[1
,2
]}

Zhou, Jingping ^{[3
]}

Sang, Guobiao ^{[2
]}

Huang, Ruilong ^{[1
]}

Zhao, Xinzhe ^{[1
]}

Fang, Jintao ^{[2
]}

Wang, Tiexin ^{[1
]}

Li, Bohan ^{[1
]}

机构：

[1] Nanjing Univ Aeronaut & Astronaut, Coll Artificial Intelligence & Comp Sci & Technol, Nanjing 211106, Peoples R China

[2] Beijing Shenzhou Aerosp Software Technol Co Ltd, Beijing 100094, Peoples R China

[3] Beijing Inst Telemetry, Beijing 100083, Peoples R China

来源：

WEB INFORMATION SYSTEMS AND APPLICATIONS, WISA 2024 | 2024年 / 14883卷

关键词：

Artificial intelligence; Natural language understanding; Vector space model; Topic model; Neural network; Deep learning; VECTOR-SPACE-MODEL;

D O I：

10.1007/978-981-97-7707-5_29

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Since the Turing Test was proposed in the 1950s, humanity began exploring artificial intelligence, with an aim to bridge the interaction gap between machines and human language. This exploration enables machines to comprehend how humans acquire, produce, and understand language, as well as the relationship between linguistic expression and the world. The paper explores the basic principles of natural language representation, the formalization of natural language, and the modeling methods of language models. The paper analyzes, summarizes and compares the mainstream technologies and methods, including vector space-based, topic model-based, graph-based, and neural network-based approaches. And how to improve the development trend and direction of language model understanding ability is predicted and further discussed.

引用

页码：331 / 363

页数：33

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