6 citations · 7 across the 6 of their papers we have counts for
7 papers
Limited Preference Data? Learning Better Reward Model with Latent Space Synthesis
Leitian Tao, Xuefeng Du, Sharon Li
Reward modeling, crucial for aligning large language models (LLMs) with human preferences, is often bottlenecked by the high cost of preference data. Existing textual data synthesi…
CodeLutra: Boosting LLM Code Generation via Preference-Guided Refinement
Leitian Tao, Xiang Chen, Tong Yu +4
Large Language Models (LLMs) have revolutionized code generation but require significant resources and often over-generalize, limiting their task-specific efficiency. Fine-tuning s…
Challenges and Future Directions of Data-Centric AI Alignment
Min-Hsuan Yeh, Jeffrey Wang, Xuefeng Du +4
As AI systems become increasingly capable and influential, ensuring their alignment with human values, preferences, and goals has become a critical research focus. Current alignmen…
Your Weak LLM is Secretly a Strong Teacher for Alignment
Leitian Tao, Yixuan Li
The burgeoning capabilities of large language models (LLMs) have underscored the need for alignment to ensure these models act in accordance with human values and intentions. Exist…
Activate and Reject: Towards Safe Domain Generalization under Category Shift
Chaoqi Chen, Luyao Tang, Leitian Tao +4
Albeit the notable performance on in-domain test points, it is non-trivial for deep neural networks to attain satisfactory accuracy when deploying in the open world, where novel do…
Non-Parametric Outlier Synthesis
Leitian Tao, Xuefeng Du, Xiaojin Zhu +1
Out-of-distribution (OOD) detection is indispensable for safely deploying machine learning models in the wild. One of the key challenges is that models lack supervision signals fro…
Pyramid Feature Alignment Network for Video Deblurring
Leitian Tao, Zhenzhong Chen
Video deblurring remains a challenging task due to various causes of blurring. Traditional methods have considered how to utilize neighboring frames by the single-scale alignment f…