Artificial Intelligence

    

LLM Survey Paper Landscape: Predicting Taxonomies

Authors: Daniel Uranga

In this study, we analyze a dataset of survey papers on Large Language Models (LLMs) published over the last 3 years to gain insights into the current trends surrounding LLMs. Primarily we analyze the author landscape and the effectiveness at predicting the taxonomies of the surveys from their title, summary, and listed categories. I find that the amount of surveys released has increased drastically in the last three years. Also, most surveys have around 8 authors, but each author appears only on one survey usually. This indicates the research is spread widely between those in the field. Finally, our investigation into predicting taxonomies was a failure with the machine learning methods we applied. However, valuable insights about the dataset can be gained from the attempts.

Comments: 4 Pages.

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Submission history

[v1] 2024-10-17 23:13:42

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