Mapping the research landscape of Large Language Models from 2018 to 2024: A bibliometric analysis

Abstract

Large language models (LLMs), built upon transformer-based architectures, have played a central role in the development of artificial intelligence since 2018. With applications extending to healthcare, education, and scientific domains, interest in LLMs has grown significantly. This study conducts a comprehensive bibliometric analysis of LLM-related publications from 2018 to 2024, based on data retrieved from the Web of Science Core Collection. A total of 24,918 records were examined through performance analysis and science mapping. The analysis covers publication trends, subject areas, publication venues, international collaborations, and keyword co-occurrence patterns. The findings reveal rapid growth and thematic diversification in the field, offering valuable insights for researchers and policymakers seeking to understand current developments and guide future research directions.

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