Large Language Models and Applications: The Rebirth of Enterprise Knowledge Management and the Rise of Prompt Libraries

被引：2
|
作者：
O'Leary, Daniel E. [1 ]
机构：
[1] Univ Southern Calif, Marshall Sch Business, Los Angeles, CA 90089 USA
来源：
IEEE INTELLIGENT SYSTEMS | 2024年 / 39卷 / 02期
关键词：
Knowledge management; Cognition; Intelligent systems; Large language models; Enterprise resource planning; Libraries;
D O I：
10.1109/MIS.2024.3366648
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This article investigates how large language systems and the apps developed for them provide a platform for enterprise knowledge management. For those resulting systems to provide consistent and accurate responses for knowledge management, enterprises are using different approaches in their prompts, such as few-shot learning, specification of purpose, and chain-of-thought reasoning. As better and more successful prompts are being built, they are being captured and prompt libraries are being created.
引用
收藏
页码：72 / 75
页数：4
相关论文
共 50 条
  • [31] Large language models for oncological applications
    Sorin, Vera
    Barash, Yiftach
    Konen, Eli
    Klang, Eyal
    JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY, 2023, 149 (11) : 9505 - 9508
  • [32] Applications of large language models in oncology
    Loefflert, Chiara M.
    Bressem, Keno K.
    Truhn, Daniel
    ONKOLOGIE, 2024, 30 (05): : 388 - 393
  • [33] Applications of Large Language Models in Pathology
    Cheng, Jerome
    BIOENGINEERING-BASEL, 2024, 11 (04):
  • [34] Large language models and their applications in bioinformatics
    Sarumi, Oluwafemi A.
    Heider, Dominik
    COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2024, 23 : 3498 - 3505
  • [35] The Effect of Prompt Types on Text Summarization Performance With Large Language Models
    Borhan, Iffat
    Bajaj, Akhilesh
    JOURNAL OF DATABASE MANAGEMENT, 2024, 35 (01)
  • [36] Empowerment of Large Language Models in Psychological Counseling through Prompt Engineering
    Huang, Shanshan
    Fu, Fuxiang
    Yang, Ke
    Zhang, Ke
    Yang, Fan
    2024 IEEE 4TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND ARTIFICIAL INTELLIGENCE, SEAI 2024, 2024, : 220 - 225
  • [37] IAPT: Instruction-Aware Prompt Tuning for Large Language Models
    Zhu, Wei
    Tian, Aaron Xuxiang
    Yin, Congrui
    Ni, Yuan
    Wang, Xiaoling
    Xie, Guotong
    PROCEEDINGS OF THE 62ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1: LONG PAPERS, 2024, : 14285 - 14304
  • [38] Attack Prompt Generation for Red Teaming and Defending Large Language Models
    Deng, Boyi
    Wang, Wenjie
    Feng, Fuli
    Deng, Yang
    Wang, Qifan
    He, Xiangnan
    FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS - EMNLP 2023, 2023, : 2176 - 2189
  • [39] Select, Prompt, Filter: Distilling Large Language Models for Summarizing Conversations
    Pham, Minh-Quang
    Indurthi, Sathish Reddy
    Chollampatt, Shamil
    Turchi, Marco
    2023 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2023), 2023, : 12257 - 12265
  • [40] Assessing the Impact of Prompt Strategies on Text Summarization with Large Language Models
    Onan, Aytug
    Alhumyani, Hesham
    COMPUTER APPLICATIONS IN INDUSTRY AND ENGINEERING, CAINE 2024, 2025, 2242 : 41 - 55
12345