7 papers
Cat-DPO: Category-Adaptive Safety Alignment
Tiankai Yang, Yi Nian, Xinyuan Li +6
Aligning large language models with human preferences must balance two competing goals: responding helpfully to legitimate requests and reliably refusing harmful ones. Most prefere…
CoAct: Co-Active LLM Preference Learning with Human-AI Synergy
Ruiyao Xu, Mihir Parmar, Tiankai Yang +3
Learning from preference-based feedback has become an effective approach for aligning LLMs across diverse tasks. However, high-quality human-annotated preference data remains expen…
HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification
Ziqing Wang, Kaize Ding
Node classification on text-attributed graphs (TAGs) is a fundamental task with broad applications in citation analysis, social networks, and recommendation systems. Current GNN-ba…
GNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedback
Ruiyao Xu, Kaize Ding
Large Language Models (LLMs) have shown strong performance on text-attributed graphs (TAGs) due to their superior semantic understanding ability on textual node features. However,…
A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimization
Ziqing Wang, Kexin Zhang, Zihan Zhao +4
Large language models (LLMs) are introducing a paradigm shift in molecular discovery by enabling text-guided interaction with chemical spaces through natural language, symbolic not…
A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation
Kexin Zhang, Shuhan Liu, Song Wang +6
Distribution shifts on graphs -- the discrepancies in data distribution between training and employing a graph machine learning model -- are ubiquitous and often unavoidable in rea…