Notes on On the Origin of LLMs: An Evolutionary Tree and Graph for 15,821 Large Language Models
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Link to paper: https://arxiv.org/abs/2307.09793
Paper published on: 2023-07-19
Paper's authors: Sarah Gao, Andrew Kean Gao
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The ELI5 TLDR:
The researchers studied a large number of text generation models called large language models (LLMs) and developed a web application called Constellation to explore and understand them. They used techniques like hierarchical clustering and n-grams to analyze the relationships between different LLMs. The application generates visualizations like dendrograms, graphs, and word clouds to help understand the landscape of LLMs. They found a weak correlation between the number of likes and downloads a model receives and identified different families of LLMs. They also introduced a new LLM called Llama that can run on a laptop, making it more accessible. The researchers hope that tools like Constellation will help researchers and developers keep up with the evolving landscape of LLMs and lead to the development of more efficient models. They also provided insights into the most common words and phrases among LLMs, which could inform the development of new models.
The Deeper Dive:
Large Language Models (LLMs) and the Landscape of Text Generation Models
The landscape of AI and machine learning is evolving at a rapid pace, with large language models (LLMs) like ChatGPT and Bard gaining prominence. With nearly 16,000 text generation models available on Hugging Face, a repository of machine learning models and datasets, navigating this vast landscape can be daunting. This paper shines a light on this landscape by employing various techniques and tools to analyze, cluster, and visualize these LLMs, providing a unique perspective on the ecosystem.
Hierarchical Clustering and N-grams
Hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. It starts by treating each observation as a separate cluster and then successively merging or splitting clusters based on a certain criterion. In the context of this paper, the researchers used hierarchical clustering to identify communities and clusters among LLMs.
N-grams, on the other hand, are contiguous sequences of n items from a given sample of text or speech. They are widely used in natural language processing and computational linguistics. In this research, n-grams were used in tandem with hierarchical clustering to better understand the relationships between different LLMs.
Constellation: A Public Web Application
The researchers developed a web application called Constellation to navigate and explore the 15,821 LLMs. This application generates various visualizations, such as dendrograms, graphs, word clouds, and scatter plots, to aid in understanding the landscape of LLMs. The dataset created by the researchers, which includes model names, number of downloads, number of likes, and model parameters, will be publicly shared on Github.
Libraries and Techniques Used
A wide array of libraries and techniques were used in this research, including BeautifulSoup, Pandas, Streamlit, Scipy, Plotly, Numpy, Scikit-learn, Radial Tree, NLTK, Matplotlib, Python-Louvain, and NetworkX.
BeautifulSoup and Pandas were used for data collection and manipulation, Streamlit for developing the web application, Scipy and Numpy for data analysis, Plotly and Matplotlib for data visualization, and Scikit-learn for machine learning tasks. NLTK, a platform for building Python programs to work with human language data, was used for text processing. Python-Louvain and NetworkX were used for community detection and network analysis, respectively.
Analysis and Visualization
The researchers used TF-IDF (Term Frequency-Inverse Document Frequency) features and hierarchical clustering to analyze the dataset and generate visualizations. TF-IDF is a numerical statistic that reflects how important a word is to a document in a collection or corpus.
They also used agglomerative clustering, another type of hierarchical clustering method, word clouds, and graph visualization with communities to further explore the data.
Findings and Acknowledgements
The researchers found a weak positive correlation between the number of likes and downloads a model receives. They also identified families of LLMs such as Wizard, Pythia, CausalLM, and Bloom and used the Louvain method to detect communities among the models.
However, they acknowledge that their approach assumes that LLMs with similar names are similar, which may not always be true.
Application and Future Prospects
The researchers developed a web application to explore the data, which includes a dendrogram, word clouds, and a graph. The application also displays statistics and an interactive scatter plot of likes versus downloads.
They hope that tools like Constellation will help researchers and developers keep pace with the rapidly evolving landscape of LLMs. This could potentially lead to the development of more efficient and effective LLMs, as well as the discovery of novel applications of these models in various domains.
Llama: A New LLM
The research introduces a new LLM called Llama, which can run on a laptop. This could potentially democratize the use of LLMs, making them accessible to a wider audience.
Most Common Words and Phrases
The research includes a table showing the most common words and phrases among all Hugging Face LLMs. This could provide insights into the most popular topics and themes in the LLM landscape, which could in turn inform the development of new models.
