5 papers
LA-CPD: Local-Evidence-Aware Change-Point Detection for Human-LLM Authorship Segmentation
Qing Yang, Zhenyu Mao, Zixiang Luo +5
As LLM-generated text becomes increasingly human-like, accurately localizing LLM-authored spans in human-LLM co-authored documents is important for attribution and accountability i…
C-HAT-Bench: Benchmarking Chinese AI-Text Detection Beyond Fully Generated Text
Qing Yang, Zixiang Luo, Zhenyu Mao +6
Large Language Models (LLMs) increasingly participate in writing by modifying or extending human drafts, causing machine involvement to vary in both form and extent. Yet most Machi…
Trajectory-Guided Forget-Recover Network for Continual LLM Unlearning
Zezheng Wu, Xinghe Cheng, Qinggang Zhang +4
Machine unlearning aims to eliminate the influence of sensitive data on a model. In the real world, unlearning requests arrive continually, which gives rise to two challenges. Firs…
U-CAN: Utility-Aware Contrastive Attenuation for Efficient Unlearning in Generative Recommendation
Zezheng Wu, Rui Wang, Xinghe Cheng +4
Generative Recommendation (GenRec) typically leverages Large Language Models (LLMs) to redefine personalization as an instruction-driven sequence generation task. However, fine-tun…
BamaER: A Behavior-Aware Memory-Augmented Model for Exercise Recommendation
Qing Yang, Yuhao Jiang, Rui Wang +6
Exercise recommendation focuses on personalized exercise selection conditioned on students' learning history, personal interests, and other individualized characteristics. Despite…