title: On the Origin of LLMs: An Evolutionary Tree and Graph for 15,821 Large
    Language Models
summary: 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/.
paper_authors._id: 64b8b167894eb78e394ff9e9
paper_authors.name: Sarah Gao
paper_authors._id: 64b8b167894eb78e394ff9ea
paper_authors.name: Andrew Kean Gao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-20T09:52:05.465Z
paper_authors._id: 63773bd50248f877527a9a3d
paper_authors.avatarUrl: /avatars/e56155a00caa5d15028c55f71dfdfea5.svg
paper_authors.fullname: Andrew Gao
paper_authors.type: user
paper_authors.user: gaodrew
paper_title: On the Origin of LLMs: An Evolutionary Tree and Graph for 15,821 Large
    Language Models
paper_summary: 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/.
paper_ai_keywords: Large Language Models (LLMs) Hierarchical clustering N-gram clustering Term frequency-inverse document frequency (TF-IDF) Text Generation models Dendrograms Scatter plots