Analyzing 16,193 LLM Papers for Fun and Profits

  • Zhiqiu Xia
  • , Lang Zhu
  • , Bingzhe Li
  • , Feng Chen
  • , Qiannan Li
  • , Chunhua Liao
  • , Feiyi Wang
  • , Hang Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Large Language Models (LLMs) are reshaping the landscape of computer science research, driving significant shifts in research priorities across diverse conferences and fields. This study provides a comprehensive analysis of the publication trend of LLM-related papers in 77 top-tier computer science conferences over the past six years (2019–2024). We approach this analysis from four distinct perspectives: (1) We investigate how LLM research is driving topic shifts within major conferences. (2) We adopt a topic modeling approach to identify various areas of LLM-related topic growth and reveal the topics of concern at different conferences. (3) We explore distinct contribution patterns of academic and industrial institutions. (4) We study the influence of national origins on LLM development trajectories. Synthesizing the findings from these diverse analytical angles, we derive ten key insights that illuminate the dynamics and evolution of the LLM research ecosystem.

Original languageEnglish
Title of host publicationProceedings - 2025 IEEE Cloud Summit, Cloud-Summit 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages29-34
Number of pages6
ISBN (Electronic)9798331523626
DOIs
StatePublished - 2025
Event2025 IEEE Cloud Summit, Cloud-Summit 2025 - Washington, United States
Duration: Jun 26 2025Jun 27 2025

Publication series

NameProceedings - 2025 IEEE Cloud Summit, Cloud-Summit 2025

Conference

Conference2025 IEEE Cloud Summit, Cloud-Summit 2025
Country/TerritoryUnited States
CityWashington
Period06/26/2506/27/25

Funding

We thank the anonymous reviewers for their helpful suggestions and feedback. This work was in part supported by NSF CyberTraining Award 2417750 and NSF Awards 2417747 and 2417748. It was also prepared by LLNL under Contract DE-AC52-07NA27344 (LLNL-CONF-2006445). This material was based upon work supported by the U.S. Dept. of Energy, Office of Science, Advanced Scientific Computing Research (SC-21). The United States Government retains, and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.

Keywords

  • Large language model (LLM)
  • bibliometrics
  • topic modeling

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