1 citations · 1 across the 6 of their papers we have counts for
6 papers
Towards Reward Fairness in RLHF: From a Resource Allocation Perspective
Sheng Ouyang, Yulan Hu, Ge Chen +3
Rewards serve as proxies for human preferences and play a crucial role in Reinforcement Learning from Human Feedback (RLHF). However, if these rewards are inherently imperfect, exh…
GUNDAM: Aligning Large Language Models with Graph Understanding
Sheng Ouyang, Yulan Hu, Ge Chen +1
Large Language Models (LLMs) have achieved impressive results in processing text data, which has sparked interest in applying these models beyond textual data, such as graphs. In t…
Preserving Node Distinctness in Graph Autoencoders via Similarity Distillation
Ge Chen, Yulan Hu, Sheng Ouyang +2
Graph autoencoders (GAEs), as a kind of generative self-supervised learning approach, have shown great potential in recent years. GAEs typically rely on distance-based criteria, su…
Towards Comprehensive Preference Data Collection for Reward Modeling
Yulan Hu, Qingyang Li, Sheng Ouyang +6
Reinforcement Learning from Human Feedback (RLHF) facilitates the alignment of large language models (LLMs) with human preferences, thereby enhancing the quality of responses gener…
Exploring Task Unification in Graph Representation Learning via Generative Approach
Yulan Hu, Sheng Ouyang, Zhirui Yang +4
Graphs are ubiquitous in real-world scenarios and encompass a diverse range of tasks, from node-, edge-, and graph-level tasks to transfer learning. However, designing specific tas…
Graph Ranking Contrastive Learning: A Extremely Simple yet Efficient Method
Yulan Hu, Sheng Ouyang, Jingyu Liu +6
Graph contrastive learning (GCL) has emerged as a representative graph self-supervised method, achieving significant success. The currently prevalent optimization objective for GCL…