构建大模型供应链图谱，追踪模型与数据集的来源关系。
HuggingGraph: Understanding the Supply Chain of LLM Ecosystem
- 设计系统化方法采集大模型供应链信息
- 构建包含40万节点、46万边的异构有向图
- 揭示模型与数据间的依赖关系，助力风险检测
大型语言模型（LLMs）利用深度学习架构处理和预测词序列，可完成翻译、摘要、问答和内容生成等多种自然语言任务。由于现有模型常基于基础模型或预训练模型，并使用外部数据集，不可避免地会继承先前模型或数据集中的漏洞、偏见或恶意组件。因此，理解这些组件的来源与发展过程对检测潜在风险、提升模型公平性及确保合规至关重要。本项目旨在研究模型与数据集之间的关系，它们是大模型供应链的核心。首先，我们设计了一种系统化的方法来收集大模型供应链信息；其次，我们构建了一个新的图结构，用于建模模型与数据集之间的关系，该图是包含402,654个节点和462,524条边的有向异构图；最后，我们进行了多种分析并得出多个有趣发现。
原文摘要 · Abstract (English)
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.
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