6 papers
TPA: Next Token Probability Attribution for Detecting Hallucinations in RAG
Pengqian Lu, Jie Lu, Anjin Liu +1
Detecting hallucinations in Retrieval-Augmented Generation remains a challenge. Prior approaches attribute hallucinations to a binary conflict between internal knowledge stored in…
Generalized Incremental Learning under Concept Drift across Evolving Data Streams
En Yu, Jie Lu, Guangquan Zhang
Real-world data streams exhibit inherent non-stationarity characterized by concept drift, posing significant challenges for adaptive learning systems. While existing methods addres…
Autonomous Concept Drift Threshold Determination
Pengqian Lu, Jie Lu, Anjin Liu +2
Existing drift detection methods focus on designing sensitive test statistics. They treat the detection threshold as a fixed hyperparameter, set once to balance false alarms and la…
Drift-aware Collaborative Assistance Mixture of Experts for Heterogeneous Multistream Learning
En Yu, Jie Lu, Kun Wang +2
Learning from multiple data streams in real-world scenarios is fundamentally challenging due to intrinsic heterogeneity and unpredictable concept drifts. Existing methods typically…
MiraGe: Multimodal Discriminative Representation Learning for Generalizable AI-Generated Image Detection
Kuo Shi, Jie Lu, Shanshan Ye +2
Recent advances in generative models have highlighted the need for robust detectors capable of distinguishing real images from AI-generated images. While existing methods perform w…
Learning Robust Spectral Dynamics for Temporal Domain Generalization
En Yu, Jie Lu, Xiaoyu Yang +2
Modern machine learning models struggle to maintain performance in dynamic environments where temporal distribution shifts, \emph{i.e., concept drift}, are prevalent. Temporal Doma…