2 citations · 2 across the 7 of their papers we have counts for
7 papers
Fine-Tuning Low-Bit Models with Gradient in Quantized Code Space
Shiguang Wu, Zhouchen Lin, Quanming Yao
Fine-tuning Low-bit models aims to adapt a quantized model while keeping the final deployed checkpoint in the same low-bit form. This setting is practically important as it reduces…
Self-Generative Adversarial Fine-Tuning for Large Language Models
Shiguang Wu, Yaqing Wang, Quanming Yao
Fine-tuning large language models (LLMs) for alignment typically relies on supervised fine-tuning or reinforcement learning from human feedback, both limited by the cost and scarci…
Asking LLMs to Verify First is Almost Free Lunch
Shiguang Wu, Quanming Yao
To enhance the reasoning capabilities of Large Language Models (LLMs) without high costs of training, nor extensive test-time sampling, we introduce Verification-First (VF), a stra…
Language Model Networks: Supervision-Efficient Learning through Dense Communication
Shiguang Wu, Yaqing Wang, Quanming Yao
Language models are increasingly used not only as standalone predictors but also as components in larger inference systems, from test-time scaling to multi-agent collaboration. We…
Learning to Learn with Contrastive Meta-Objective
Shiguang Wu, Yaqing Wang, Yatao Bian +1
Meta-learning enables learning systems to adapt quickly to new tasks, similar to humans. Different meta-learning approaches all work under/with the mini-batch episodic training fra…
PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction
Shiguang Wu, Yaqing Wang, Quanming Yao
Molecular property prediction (MPP) plays a crucial role in biomedical applications, but it often encounters challenges due to a scarcity of labeled data. Existing works commonly a…
ColdNAS: Search to Modulate for User Cold-Start Recommendation
Shiguang Wu, Yaqing Wang, Qinghe Jing +3
Making personalized recommendation for cold-start users, who only have a few interaction histories, is a challenging problem in recommendation systems. Recent works leverage hypern…