6 papers
When to Trust: A Causality-Aware Calibration Framework for Accurate Knowledge Graph Retrieval-Augmented Generation
Jing Ren, Bowen Li, Ziqi Xu +3
Knowledge Graph Retrieval-Augmented Generation (KG-RAG) extends the RAG paradigm by incorporating structured knowledge from knowledge graphs, enabling Large Language Models (LLMs)…
When to Invoke: Refining LLM Fairness with Toxicity Assessment
Jing Ren, Bowen Li, Ziqi Xu +6
Large Language Models (LLMs) are increasingly used for toxicity assessment in online moderation systems, where fairness across demographic groups is essential for equitable treatme…
LiteFat: Lightweight Spatio-Temporal Graph Learning for Real-Time Driver Fatigue Detection
Jing Ren, Suyu Ma, Hong Jia +5
Detecting driver fatigue is critical for road safety, as drowsy driving remains a leading cause of traffic accidents. Many existing solutions rely on computationally demanding deep…
Causal Prompting for Implicit Sentiment Analysis with Large Language Models
Jing Ren, Wenhao Zhou, Bowen Li +7
Implicit Sentiment Analysis (ISA) aims to infer sentiment that is implied rather than explicitly stated, requiring models to perform deeper reasoning over subtle contextual cues. W…
Foundation Models for Anomaly Detection: Vision and Challenges
Jing Ren, Tao Tang, Hong Jia +6
As data continues to grow in volume and complexity across domains such as finance, manufacturing, and healthcare, effective anomaly detection is essential for identifying irregular…
Factor Graph-based Interpretable Neural Networks
Yicong Li, Kuanjiu Zhou, Shuo Yu +4
Comprehensible neural network explanations are foundations for a better understanding of decisions, especially when the input data are infused with malicious perturbations. Existin…