Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·May 28·5 min read
Snippet-Driven Supply Chain Discovery with LLMs: Scaling Visibility in China
TL;DR
Supply chain visibility in China is often limited due to fragmented data sources and lack of integration. A new approach using Large Language Models (LLMs) was developed to facilitate snippet-driven discovery of supply chain information.
✦ Why It Matters
Engineers can apply LLMs to improve data visibility and decision-making in complex supply chains.
Key Takeaways
- Utilize web search snippets for efficient data extraction.
- Consider LLMs for scalable relationship mapping in supply chains.
- Leverage the SCKG for deeper insights into unlisted firms.
- Ensure data provenance for auditability in supply chain databases.
How It Works
The snippet-driven method uses web search snippets as a preliminary layer for extracting inter-firm relationships. This approach allows for efficient processing by LLMs, significantly reducing the computational cost compared to full-text analysis.