What do we know about Hugging Face? A systematic literature review and quantitative validation of qualitative claims
Jason Jones, Wenxin Jiang, Nicholas Synovic, George K. Thiruvathukal,, James C. Davis

TL;DR
This paper systematically reviews and quantitatively validates claims about the Hugging Face PTM registry, revealing insights into reuse dynamics, documentation quality, and ecosystem evolution.
Contribution
It provides the first systematic synthesis and quantitative validation of claims about Hugging Face PTM reuse, bridging qualitative assertions with empirical data.
Findings
PTMs have a higher turnover rate than traditional software.
Strong correlation between documentation quality and PTM popularity.
Validated 3 out of 4 qualitative claims through quantitative analysis.
Abstract
Background: Collaborative Software Package Registries (SPRs) are an integral part of the software supply chain. Much engineering work synthesizes SPR package into applications. Prior research has examined SPRs for traditional software, such as NPM (JavaScript) and PyPI (Python). Pre-Trained Model (PTM) Registries are an emerging class of SPR of increasing importance, because they support the deep learning supply chain. Aims: Recent empirical research has examined PTM registries in ways such as vulnerabilities, reuse processes, and evolution. However, no existing research synthesizes them to provide a systematic understanding of the current knowledge. Some of the existing research includes qualitative claims lacking quantitative analysis. Our research fills these gaps by providing a knowledge synthesis and quantitative analyses. Methods: We first conduct a systematic literature…
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Taxonomy
TopicsEmotions and Moral Behavior · Psychology of Social Influence · Psychological Testing and Assessment
MethodsSurrogate Lagrangian Relaxation
