mlsanityData to 7 Oct 2026

HuggingGraph: Understanding the Supply Chain of LLM Ecosystem

arXiv 2507.14240 ↗First posted 17 Jul 2025cs.CL cs.AIcited by 8

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Abstract (arXiv)

Large language models (LLMs) leverage deep learning architectures to process and predict sequences of words, enabling them to perform a wide range of natural language processing tasks, such as translation, summarization, question answering, and content generation. As existing LLMs are often built from base models or other pre-trained models and use external datasets, they can inevitably inherit vulnerabilities, biases, or malicious components that exist in previous models or datasets. Therefore, it is critical to understand these components' origin and development process to detect potential risks, improve model fairness, and ensure compliance with regulatory frameworks. Motivated by that, this project aims to study such relationships between models and datasets, which are the central parts of the LLM supply chain. First, we design a methodology to systematically collect LLMs' supply chain information. Then, we design a new graph to model the relationships between models and datasets, which is a directed heterogeneous graph, having 402,654 nodes and 462,524 edges. Lastly, we perform different types of analysis and make multiple interesting findings.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 27 of its 73 references that are in the map. First posted on arXiv 17 Jul 2025.

Builds on

27 of 73 references.

References · 27

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2021Pre-Trained Models: Past, Present and Futurecited by 319
  4. 2023State of What Art? A Call for Multi-Prompt LLM Evaluationcited by 299
  5. 2018A Survey on Deep Learning for Named Entity Recognitioncited by 281
  6. 2024Evolutionary Optimization of Model Merging Recipescited by 204
  7. 2023Large Language Models as General Pattern Machinescited by 196
  8. 2022Will we run out of data? Limits of LLM scaling based on human-generated datacited by 187
  9. 2024Llama Scope: Extracting Millions of Features from Llama-3.1-8B with Sparse Autoencoderscited by 133
  10. 2024A Survey of Resource-efficient LLM and Multimodal Foundation Modelscited by 84
  11. 2024SafetyPrompts: a Systematic Review of Open Datasets for Evaluating and Improving Large Language Model Safetycited by 75
  12. 2023SlimPajama-DC: Understanding Data Combinations for LLM Trainingcited by 72
15 more
  1. 2020Kaggle forecasting competitions: An overlooked learning opportunitycited by 55
  2. 2023CodeLMSec Benchmark: Systematically Evaluating and Finding Security Vulnerabilities in Black-Box Code Language Modelscited by 52
  3. 2023An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registrycited by 45
  4. 2024Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuningcited by 31
  5. 2024Large Language Model Supply Chain: A Research Agendacited by 23
  6. 2024Increasing the LLM Accuracy for Question Answering: Ontologies to the Rescue!cited by 18
  7. 2024The AI Community Building the Future? A Quantitative Analysis of Development Activity on Hugging Face Hubcited by 18
  8. 2023An Empirical Study on Using Large Language Models to Analyze Software Supply Chain Security Failurescited by 11
  9. 2022Quadapter: Adapter for GPT-2 Quantizationcited by 10
  10. 2024PathoTune: Adapting Visual Foundation Model to Pathological Specialistscited by 8
  11. 2024Assessing the Zero-Shot Capabilities of LLMs for Action Evaluation in RLcited by 7
  12. 2023LLM-based Interaction for Content Generation: A Case Study on the Perception of Employees in an IT departmentcited by 6
  13. 2023A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Software Engineering Taskscited by 5
  14. 2024Challenges of Using Pre-trained Models: the Practitioners' Perspectivecited by 3
  15. 2025Bingo: Radix-based Bias Factorization for Random Walk on Dynamic Graphscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 5.

8 papers.

  1. 2025LLM DNA: Tracing Model Evolution via Functional Representationscited by 15
  2. 2025Anatomy of a Machine Learning Ecosystem: 2 Million Models on Hugging Facecited by 12
  3. 2025Learning a Zeroth-Order Optimizer for Fine-Tuning LLMscited by 3
  4. 2026Test Before You Deploy: Governing Updates in the LLM Supply Chaincited by 1
  5. 2026Which Models Are Our Models Built On? Auditing Invisible Dependencies in Modern LLMscited by 1
  6. 2026MetaGAI: A Large-Scale and High-Quality Benchmark for Generative AI Model and Data Card Generationcited by 0
  7. 2026ArtifactLinker: Linking Scientific Artifacts for Automatic State-of-the-Art Discoverycited by 0
  8. 2026Witeness Overlap: Directional Provenance Inside Open-Weight Model Familiescited by 0