Global Trends and Collaboration Dynamics in Large Language Model Research: A Bibliometric Analysis

Large Language Models (LLMs) have spearheaded a significant transformation in artificial intelligence, rapidly becoming a focal point of scientific research.This study aims to comprehensively analyze the intellectual structure and temporal evolution of scientific publications on LLMs using bibliometric methods.The research data comprises 10,321 articles that met the specified criteria, retrieved from the Web of Science (WoS) Core Collection database using the search term "large language model".Performance analysis and science mapping techniques were applied using VOSviewer software for data analysis and visualization.Within this scope, co-authorship, co-citation, and keyword co-occurrence analyses were conducted to identify the key actors, collaboration networks, intellectual foundations, and primary research topics in the field.The analysis reveals that the field has shown exponential growth since 2023, is shaped by the leadership of the USA, and China and is rapidly expanding beyond its theoretical core into applied areas like medicine and education.