Exploring LLM Research Trends and Insights for Enhanced Accessibility

Abstract

The field of Large Language Models (LLMs) has seen rapid development, with numerous survey papers being published with progress. This technical report presents an exploration and analysis of recent survey studies on LLMs. As LLMs are gaining increasing attention, beginners are mostly relying on survey papers to understand the advancement of this area. However, the immense number of survey papers published in recent years pose a challenge to newcomers. With the goal of facilitating more accessible learning, this work investigates the statistics of these survey articles. The report covers data exploration, manipulation, visualization, and evaluation of key metadata elements such as taxonomy, release dates, and categories. Different techniques are employed to pre-process the dataset and machine learning techniques are applied to analyze the data to offer a comprehensive understanding of the dataset. Finally a classification of survey papers based on taxonomoy is carried out using Logistic Regression classification model. The aim is to provide insights into which areas of LLMs research have been emphasized, how publication trends have evolved, and how the content of survey papers is structured.