5 papers
A Role-Based LLM Framework for Structured Information Extraction from Healthy Food Policies
Congjing Zhang, Ruoxuan Bao, Jingyu Li +3
Current Large Language Model (LLM) approaches for information extraction (IE) in the healthy food policy domain are often hindered by various factors, including misinformation, spe…
Team, Then Trim: An Assembly-Line LLM Framework for High-Quality Tabular Data Generation
Congjing Zhang, Ryan Feng Lin, Ruoxuan Bao +1
While tabular data is fundamental to many real-world machine learning (ML) applications, acquiring high-quality tabular data is usually labor-intensive and expensive. Limited by th…
CrowdLLM: Building LLM-Based Digital Populations Augmented with Generative Models
Ryan Feng Lin, Keyu Tian, Hanming Zheng +3
The emergence of large language models (LLMs) has sparked much interest in creating LLM-based digital populations that can be applied to many applications such as social simulation…
ALARM: Automated MLLM-Based Anomaly Detection in Complex-EnviRonment Monitoring with Uncertainty Quantification
Congjing Zhang, Feng Lin, Xinyi Zhao +5
The advance of Large Language Models (LLMs) has greatly stimulated research interest in developing multi-modal LLM (MLLM)-based visual anomaly detection (VAD) algorithms that can b…
SmartHome-Bench: A Comprehensive Benchmark for Video Anomaly Detection in Smart Homes Using Multi-Modal Large Language Models
Xinyi Zhao, Congjing Zhang, Pei Guo +4
Video anomaly detection (VAD) is essential for enhancing safety and security by identifying unusual events across different environments. Existing VAD benchmarks, however, are prim…