5 papers
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…
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…
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…
Multimodal Large Language Models with Adaptive Preference Optimization for Sequential Recommendation
Yu Wang, Yonghui Yang, Le Wu +3
Recent advances in Large Language Models (LLMs) have opened new avenues for sequential recommendation by enabling natural language reasoning over user behavior sequences. A common…
Debate over Mixed-knowledge: A Robust Multi-Agent Reasoning Framework for Incomplete Knowledge Graph Question Answering
Jilong Liu, Pengyang Shao, Wei Qin +3
Knowledge Graph Question Answering (KGQA) aims to improve factual accuracy by leveraging structured knowledge. However, real-world Knowledge Graphs (KGs) are often incomplete, lead…