# SHIELD: LLM-Driven Schema Induction for Predictive Analytics in EV Battery Supply Chain Disruptions

arXiv: https://arxiv.org/abs/2408.05357 · HTML: https://mlsanity.com/paper/2408.05357/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 42 references that are in the map.
- First posted on arXiv 9 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Rationale-Augmented Ensembles in Language Models](https://mlsanity.com/paper/2207.00747/) (2022)
- [Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning](https://mlsanity.com/paper/2305.16646/) (2023)
- [Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding](https://mlsanity.com/paper/2110.07476/) (2021)
- [Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification](https://mlsanity.com/paper/2307.01972/) (2023)
- [Human-in-the-Loop Schema Induction](https://mlsanity.com/paper/2302.13048/) (2023)

## Built on by (most cited first, 12 of 12)

- [Towards Unified World Models for Visual Navigation via Memory-Augmented Planning and Foresight](https://mlsanity.com/paper/2510.08713/) (2025)
- [Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions](https://mlsanity.com/paper/2504.11967/) (2025)
- [Language-Conditioned World Modeling for Visual Navigation](https://mlsanity.com/paper/2603.26741/) (2026)
- [HA-VLN 2.0: An Open Benchmark and Leaderboard for Human-Aware Navigation in Discrete and Continuous Environments with Dynamic Multi-Human Interactions](https://mlsanity.com/paper/2503.14229/) (2025)
- [LOGOS: LLM-driven End-to-End Grounded Theory Development and Schema Induction for Qualitative Research](https://mlsanity.com/paper/2509.24294/) (2025)
- [Emotion-LLaMAv2 and MMEVerse: A New Framework and Benchmark for Multimodal Emotion Understanding](https://mlsanity.com/paper/2601.16449/) (2026)
- [Battery State of Health Estimation Using LLM Framework](https://mlsanity.com/paper/2501.18123/) (2025)
- [Automating Supply Chain Disruption Monitoring via an Agentic AI Approach](https://mlsanity.com/paper/2601.09680/) (2026)
- [Forecasting Supply Chain Disruptions with Foresight Learning](https://mlsanity.com/paper/2604.01298/) (2026)
- [Fine-Grained Customized Fashion Design with Image-into-Prompt benchmark and dataset from LMM](https://mlsanity.com/paper/2509.09324/) (2025)
- [GenAI-Driven Approach to RISC-V Supply Chain Exploration](https://mlsanity.com/paper/2605.15223/) (2026)
- [Predicting the Financial Impact of Supply Chain Risk for Major AI-Related Semiconductor Firms: A Heterogeneous Graph Patch Transformer Approach](https://mlsanity.com/paper/2609.32741/) (2026)

## Abstract (arXiv)

The electric vehicle (EV) battery supply chain's vulnerability to disruptions necessitates advanced predictive analytics. We present SHIELD (Schema-based Hierarchical Induction for EV supply chain Disruption), a system integrating Large Language Models (LLMs) with domain expertise for EV battery supply chain risk assessment. SHIELD combines: (1) LLM-driven schema learning to construct a comprehensive knowledge library, (2) a disruption analysis system utilizing fine-tuned language models for event extraction, multi-dimensional similarity matching for schema matching, and Graph Convolutional Networks (GCNs) with logical constraints for prediction, and (3) an interactive interface for visualizing results and incorporating expert feedback to enhance decision-making. Evaluated on 12,070 paragraphs from 365 sources (2022-2023), SHIELD outperforms baseline GCNs and LLM+prompt methods (e.g., GPT-4o) in disruption prediction. These results demonstrate SHIELD's effectiveness in combining LLM capabilities with domain expertise for enhanced supply chain risk assessment.