Exploring the Taxonomy of Survey Papers on Large Language Models Using Classical Machine Learning

Abstract

The rapid advancements in large language models (LLMs) have led to an exponential increase in survey papers, making it challenging to systematically track and analyze their evolving taxonomy. This study employs graph representation learning combined with classical machine learning techniques to model and interpret the structural evolution of LLM-related survey papers. By constructing attributed graphs that capture topic distributions and interconnections, we provide a data-driven framework to explore research trends in this domain. A dataset of 241 survey papers published between July 2021 and January 2024 is analyzed to identify thematic developments and interdisciplinary relationships. The results highlight key areas of specialization, including the emergence of prompting science, multimodal models, and domain-specific applications in finance, education, and law. Co-occurrence analysis of survey topics reveals strong interconnections between core LLM research and fields such as software engineering, hardware architecture, and evaluation methodologies. These findings demonstrate the increasing specialization of LLM research and its growing integration across multiple disciplines. By leveraging graph-based methodologies, this study offers a structured approach to understanding the LLM survey landscape, facilitating efficient navigation of existing literature and identification of emerging research directions. The insights presented contribute to a more comprehensive understanding of the field’s trajectory, assisting researchers and practitioners in engaging with the latest developments in LLM research.