Trend Analysis Through Large Language Models

被引:0
|
作者
Alzapiedi, Lucas [1 ,2 ]
Bihl, Trevor [3 ]
机构
[1] Appl Res Solut, Dayton, OH 45440 USA
[2] Ohio State Univ, Columbus, OH 43210 USA
[3] Air Force Res Lab, Sensors Directorate, Wright Patterson AFB, OH USA
关键词
Artificial intelligence; natural language processing; large language models; n-grams;
D O I
10.1109/NAECON61878.2024.10670655
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
The rapid development of Artificial Intelligence (AI) technologies has outpaced the preparedness of many organizations. One challenge lies in the impracticality for individual researchers to sift through numerous research papers published annually. This study introduces a novel approach that combines n-gram analysis with Large Language Models (LLMs) for backtesting. The combined approach uses n-gram analysis from a corpus and then applies the LLMs to verify results. Example results are presented using a dataset of abstracts taken from the Web of Science. The implications of this study suggest a shift in how research trends are identified, enabling a more dynamic and responsive approach to research funding and investigation in the rapidly evolving field of AI.
引用
收藏
页码:370 / 374
页数:5
相关论文
共 50 条
  • [1] Trend Extraction and Analysis via Large Language Models
    Soru, Tommaso
    Marshall, Jim
    18TH IEEE INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING, ICSC 2024, 2024, : 285 - 288
  • [2] Trend Analysis of Large Language Models through a Developer Community: A Focus on Stack Overflow
    Son, Jungha
    Kim, Boyoung
    INFORMATION, 2023, 14 (11)
  • [3] Large language models in orthopedics: An exploratory research trend analysis and machine learning classification
    Garcia, Ausberto Velasquez
    Minami, Masataka
    Mejia-Rodriguez, Manuel
    Ortiz-Morales, Jorge Rolando
    Radice, Fernando
    JOURNAL OF ORTHOPAEDICS, 2025, 66 : 110 - 118
  • [4] Understanding Telecom Language Through Large Language Models
    Bariah, Lina
    Zou, Hang
    Zhao, Qiyang
    Mouhouche, Belkacem
    Bader, Faouzi
    Debbah, Merouane
    IEEE CONFERENCE ON GLOBAL COMMUNICATIONS, GLOBECOM, 2023, : 6542 - 6547
  • [5] ChatGeoAI: Enabling Geospatial Analysis for Public through Natural Language, with Large Language Models
    Mansourian, Ali
    Oucheikh, Rachid
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2024, 13 (10)
  • [6] Automated Analysis of Algorithm Descriptions Quality, Through Large Language Models
    Sterbini, Andrea
    Temperini, Marco
    GENERATIVE INTELLIGENCE AND INTELLIGENT TUTORING SYSTEMS, PT I, ITS 2024, 2024, 14798 : 258 - 271
  • [7] Requirements Verification Through the Analysis of Source Code by Large Language Models
    Couder, Juan Ortiz
    Gomez, Dawson
    Ochoa, Omar
    SOUTHEASTCON 2024, 2024, : 75 - 80
  • [8] Uncertainty quantification in large language models through convex hull analysis
    Catak, Ferhat Ozgur
    Kuzlu, Murat
    Discover Artificial Intelligence, 2024, 4 (01):
  • [9] Level Generation Through Large Language Models
    Todd, Graham
    Earle, Sam
    Nasir, Muhammad Umair
    Green, Michael Cerny
    Togelius, Julian
    PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON THE FOUNDATIONS OF DIGITAL GAMES, FDG 2023, 2023,
  • [10] Aligning Large Language Models through Synthetic Feedback
    Kim, Sungdong
    Bae, Sanghwan
    Shin, Jamin
    Kang, Soyoung
    Kwak, Donghyun
    Yoo, Kang Min
    Seo, Minjoon
    2023 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2023), 2023, : 13677 - 13700