On the Origin of LLMs: An Evolutionary Tree and Graph for 15,821 Large Language Models
This work addresses the problem of tracking and understanding the proliferation of LLMs for researchers and practitioners, though it is incremental as it applies existing methods to new data.
The researchers tackled the lack of a comprehensive index for the rapidly growing number of large language models (LLMs) by creating an atlas called Constellation, which successfully clusters 15,821 LLMs into meaningful families and subgroups using hierarchical clustering and provides a public web application for visualization.
Since late 2022, Large Language Models (LLMs) have become very prominent with LLMs like ChatGPT and Bard receiving millions of users. Hundreds of new LLMs are announced each week, many of which are deposited to Hugging Face, a repository of machine learning models and datasets. To date, nearly 16,000 Text Generation models have been uploaded to the site. Given the huge influx of LLMs, it is of interest to know which LLM backbones, settings, training methods, and families are popular or trending. However, there is no comprehensive index of LLMs available. We take advantage of the relatively systematic nomenclature of Hugging Face LLMs to perform hierarchical clustering and identify communities amongst LLMs using n-grams and term frequency-inverse document frequency. Our methods successfully identify families of LLMs and accurately cluster LLMs into meaningful subgroups. We present a public web application to navigate and explore Constellation, our atlas of 15,821 LLMs. Constellation rapidly generates a variety of visualizations, namely dendrograms, graphs, word clouds, and scatter plots. Constellation is available at the following link: https://constellation.sites.stanford.edu/.