Prompt Engineering: Guiding the Way to Effective Large Language Models

被引:0
|
作者
Aljanabi M. [1 ]
Yaseen M.G. [2 ]
Ali A.H. [1 ]
Mohammed M.A. [2 ]
机构
[1] Department of Computer, College of Education, Al-Iraqia University, Baghdad
关键词
Editorial; Large Language model; Prompt engineering;
D O I
10.52866/ijcsm.2023.04.04.012
中图分类号
学科分类号
摘要
Large language models (LLMs) have become prominent tools in various domains, such as natural language processing, machine translation, and the development of creative text. Nevertheless, in order to fully exploit the capabilities of Language Models, it is imperative to establish efficient communication channels between humans and machines. The discipline of engineering involves the creation of well-constructed and informative prompts, which act as a crucial link between human intention and the execution of tasks by machines. The present study examines the concept of rapid engineering, elucidating its underlying concepts, methodologies, and diverse range of practical applications. © 2023 College of Education, Al-Iraqia University. All rights reserved
引用
收藏
页码:151 / 155
页数:4
相关论文
共 50 条
  • [21] The Effect of Prompt Types on Text Summarization Performance With Large Language Models
    Borhan, Iffat
    Bajaj, Akhilesh
    Journal of Database Management, 2024, 35 (01)
  • [22] Soft prompt tuning for augmenting dense retrieval with large language models
    Peng, Zhiyuan
    Wu, Xuyang
    Wang, Qifan
    Fang, Yi
    Knowledge-Based Systems, 2025, 309
  • [23] Prompt Wrangling: On Replication and Generalization in Large Language Models for PCG Levels
    Karkaj, Arash Moradi
    Nelson, Mark J.
    Koutis, Ioannis
    Hoover, Amy K.
    PROCEEDINGS OF THE 19TH INTERNATIONAL CONFERENCE ON THE FOUNDATIONS OF DIGITAL GAMES, FDG 2024, 2024,
  • [24] A Study on Performance Improvement of Prompt Engineering for Generative AI with a Large Language Model
    Park, Daeseung
    An, Gi-taek
    Kamyod, Chayapol
    Kim, Cheong Ghil
    JOURNAL OF WEB ENGINEERING, 2023, 22 (08): : 1187 - 1206
  • [25] TrojLLM: A Black-box Trojan Prompt Attack on Large Language Models
    Xue, Jiaqi
    Zheng, Mengxin
    Hua, Ting
    Shen, Yilin
    Liu, Yepeng
    Boloni, Ladislau
    Lou, Qian
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,
  • [26] A Security Risk Taxonomy for Prompt-Based Interaction With Large Language Models
    Derner, Erik
    Batistic, Kristina
    Zahalka, Jan
    Babuska, Robert
    IEEE ACCESS, 2024, 12 : 126176 - 126187
  • [27] Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models
    Duan, Haonan
    Dziedzic, Adam
    Papernot, Nicolas
    Boenisch, Franziska
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,
  • [28] Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies
    Liu, Yilun
    Tao, Shimin
    Meng, Weibin
    Wang, Jingyu
    Ma, Wenbing
    Chen, Yuhang
    Zhao, Yanqing
    Yang, Hao
    Jiang, Yanfei
    PROCEEDINGS 2024 32ND IEEE/ACM INTERNATIONAL CONFERENCE ON PROGRAM COMPREHENSION, ICPC 2024, 2024, : 35 - 46
  • [29] Towards Taming Large Language Models with Prompt Templates for Legal GRL Modeling
    de Kinderen, Sybren
    Winter, Karolin
    ENTERPRISE, BUSINESS-PROCESS AND INFORMATION SYSTEMS MODELING, BPMDS 2024, EMMSAD 2024, 2024, 511 : 213 - 228
  • [30] DrugReAlign: a multisource prompt framework for drug repurposing based on large language models
    Jinhang Wei
    Linlin Zhuo
    Xiangzheng Fu
    XiangXiang Zeng
    Li Wang
    Quan Zou
    Dongsheng Cao
    BMC Biology, 22 (1)