Summary of Shield: Llm-driven Schema Induction For Predictive Analytics in Ev Battery Supply Chain Disruptions, by Zhi-qi Cheng et al.
SHIELD: LLM-Driven Schema Induction for Predictive Analytics in EV Battery Supply Chain Disruptions
by Zhi-Qi Cheng, Yifei Dong, Aike Shi, Wei Liu, Yuzhi Hu, Jason O’Connor, Alexander G. Hauptmann, Kate S. Whitefoot
First submitted to arxiv on: 9 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a predictive analytics system called SHIELD, which integrates Large Language Models (LLMs) and domain expertise to assess risks in the electric vehicle battery supply chain. SHIELD combines three components: schema learning using LLMs, disruption analysis using fine-tuned language models and Graph Convolutional Networks (GCNs), and an interactive interface for visualizing results and incorporating expert feedback. The system outperforms baseline GCNs and LLM+prompt methods in disruption prediction on a dataset of 12,070 paragraphs from 365 sources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SHIELD is a new way to predict disruptions in the electric vehicle battery supply chain using advanced computer models. It combines the power of these models with knowledge about the industry to make better predictions. The system does three things: it learns from a large amount of data, analyzes potential disruptions, and lets experts review the results. This helps companies make informed decisions about how to manage risks in their supply chains. |
Keywords
» Artificial intelligence » Prompt
