Lifting the Veil on the Large Language Model Supply Chain: Composition, Risks, and Mitigations

2024-10-28

arXiv.org

AI Summary

Existing research lacks a systematic characterization of the large language model (LLM) supply chain, particularly neglecting integrated structural analysis and risk governance from both supply and demand perspectives. Method: This study constructs the first end-to-end, dual-perspective (supply/demand) LLM supply chain framework, integrating systematic literature review, stakeholder mapping, root-cause risk modeling, taxonomic analysis, and industrial case studies. Contribution/Results: We propose a standardized LLM supply chain composition model; establish a three-tiered risk taxonomy encompassing 32 distinct risk actions; and design 15 targeted mitigation strategies aligned with specific actor–component–phase combinations. These contributions fill a critical gap in systematic LLM supply chain governance research, providing both an empirical foundation and a foundational paradigm for industry risk mitigation and future academic inquiry.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Large language models (LLM) have sparked significant impact with regard to both intelligence and productivity. In recent years, a great surge has been witnessed in the introduction of both commercial and open-source LLMs. Many businesses have adopted the LLMs into their applications to solve their own domain-specific tasks. However, integrating LLMs into specific business scenarios requires more than just utilizing the models themselves. Instead, it is a systematic process that involves substantial components, which are collectively referred to as the LLM supply chain. The LLM supply chain inherently carries risks. Therefore, it is essential to understand the types of components that may be introduced into the supply chain and the associated risks, enabling different stakeholders to implement effective mitigation measures. While some literature discusses risks associated with LLMs, there is currently no paper that clearly outlines the LLM supply chain from the perspective of both providing and consuming its components. As LLMs have become essential infrastructure in the new era, we believe that a thorough review of the LLM supply chain, along with its inherent risks and mitigation strategies, would be valuable for industry practitioners to avoid potential damages and losses, and enlightening for academic researchers to rethink existing approaches and explore new avenues of research. Our paper provides a comprehensive overview of the LLM supply chain, detailing the stakeholders, composing artifacts, and the supplying types. We developed taxonomies of risk types, risky actions, and mitigations related to various supply chain stakeholders and components. In summary, our work explores the technical and operational aspects of the LLM supply chain, offering valuable insights for researchers and engineers in the evolving LLM landscape.

Problem

• Understanding LLM supply chain composition and relationships
• Identifying risks and mitigations across stakeholders and components
• Bridging gaps in systematic LLM supply chain characterization

Innovation

• Systematic taxonomy for LLM supply chain risks
• Dual-perspective analysis of contributors and consumers
• Comprehensive risk mitigation measures for stakeholders