15 citations · 22 across the 18 of their papers we have counts for
7 papers · 1 filter
Suppressing Forgery-Specific Shortcuts for Generalizable Deepfake Detection
Yihui Wang, Yonghui Yang, Jilong Liu +3
Deepfake detection suffers from poor generalization across forgery methods, as existing models tend to rely on spurious method-specific shortcuts that fail to transfer to unseen ma…
MLLMRec-R1: Incentivizing Reasoning Capability in Large Language Models for Multimodal Sequential Recommendation
Yu Wang, Yonghui Yang, Le Wu +3
Group relative policy optimization (GRPO) has become a standard post-training paradigm for improving reasoning and preference alignment in large language models (LLMs), and has rec…
MealRec: Multi-granularity Sequential Modeling via Hierarchical Diffusion Models for Micro-Video Recommendation
Xinxin Dong, Haokai Ma, Yuze Zheng +3
Micro-video recommendation aims to capture user preferences from the collaborative and context information of the interacted micro-videos, thereby predicting the appropriate videos…
CLEAR: Null-Space Projection for Cross-Modal De-Redundancy in Multimodal Recommendation
Hao Zhan, Yihui Wang, Yonghui Yang +6
Multimodal recommendation has emerged as an effective paradigm for enhancing collaborative filtering by incorporating heterogeneous content modalities. Existing multimodal recommen…
CompassDPO: Dynamics-Controlled Direct Preference Optimization for Robust Safety Alignment
Jilong Liu, Yonghui Yang, Pengyang Shao +5
Direct Preference Optimization (DPO) has become a standard framework for safety alignment, but its reliance on pairwise preference updates makes training sensitive to imperfect sup…
Controllable Value Alignment in Large Language Models through Neuron-Level Editing
Yonghui Yang, Yihui Wang, Junwei Li +6
Aligning large language models (LLMs) with human values has become increasingly important as their influence on human behavior and decision-making expands. However, existing steeri…
Revisiting Robustness for LLM Safety Alignment via Selective Geometry Control
Yonghui Yang, Wenjian Tao, Jilong Liu +6
Safety alignment of large language models remains brittle under domain shift and noisy preference supervision. Most existing robust alignment methods focus on uncertainty in alignm…