Papers by Alexander Dong
Optimizing Retrieval-Augmented Generation for E-Commerce How-To Assistance (2026.acl-industry)
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| Challenge: | Recent advances in large language models (LLMs) have significantly improved Retrieval-Augmented Generation (RAG), enabling assistants that can reliably ground responses in external knowledge sources while maintaining high-quality natural language interaction. |
| Approach: | They propose a RAG-based How-To Assistant that groundes responses in a proprietary knowledge base to provide personalized customer support. |
| Outcome: | The proposed assistant can ground responses in a proprietary knowledge base while maintaining high-quality natural language interaction. |
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)
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Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji
| Challenge: | We present a new information extraction system that can construct temporal event graphs from news documents. |
| Approach: | They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction . |
| Outcome: | The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities. |
SHIELD: LLM-Driven Schema Induction for Predictive Analytics in EV Battery Supply Chain Disruptions (2024.emnlp-industry)
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Zhi-Qi Cheng, Yifei Dong, Aike Shi, Wei Liu, Yuzhi Hu, Jason O’Connor, Alexander Hauptmann, Kate Whitefoot
| Challenge: | EV battery supply chain is vulnerable to disruptions caused by natural disasters and geopolitical tensions. |
| Approach: | They propose a system integrating Large Language Models with domain expertise for EV supply chain risk assessment. |
| Outcome: | Evaluated on 12,070 paragraphs from 365 sources (2022-2023), SHIELD outperforms baseline GCNs and LLM+prompt methods in disruption prediction. |