1 citations · 1 across the 9 of their papers we have counts for
9 papers
G-CARL: Grounded Checklist-Aligned Reward Learning for Patient-Oriented Medical Report Interpretation
Shiao Xie, Siyu Chen, Jianwei Lv +3
Personalized interpretation of medical reports has emerged as an increasingly important need among patients. Addressing this need requires both evidence-grounded medical factuality…
EAPO: Entropy-Driven Adaptive Positive-Negative Sample Weighting for Policy Optimization in Open-Ended QA
Yunsheng Zeng, Gen Li, Yuwei Miao +8
Large Reasoning Models are typically trained via reinforcement learning from verifiable rewards (RLVR). However, existing approaches adopt fixed weights for positive and negative s…
Replacing Parameters with Preferences: Federated Alignment of Heterogeneous Vision-Language Models
Shule Lu, Yujing Wang, Hainan Zhang +5
Vision-Language Models (VLMs) have broad potential in privacy-sensitive domains such as healthcare and finance, yet strict data-sharing constraints render centralized training infe…
C-MIG: Multi-view Information Gain-based Retrieval-Augmented Generation for Clinical Diagnosis Reasoning
Yuwei Miao, Gen Li, Yunsheng Zeng +8
Retrieval-augmented generation (RAG) trained with reinforcement learning (RL) is a promising way to ground large language models in medical evidence for clinical diagnosis. However…
GRADE: Probing Knowledge Gaps in LLMs through Gradient Subspace Dynamics
Yujing Wang, Yuanbang Liang, Yukun Lai +2
Detecting whether a model's internal knowledge is sufficient to correctly answer a given question is a fundamental challenge in deploying responsible LLMs. In addition to verbalisi…
Replacing Parameters with Preferences: Federated Alignment of Heterogeneous Vision-Language Models
Shule Lu, Yujing Wang, Hainan Zhang +5
VLMs have broad potential in privacy-sensitive domains such as healthcare and finance, yet strict data-sharing constraints render centralized training infeasible. FL mitigates this…
Learning to Erase Private Knowledge from Multi-Documents for Retrieval-Augmented Large Language Models
Yujing Wang, Jinwen Chen, Hainan Zhang +5
Retrieval-Augmented Generation (RAG) is a promising technique for applying LLMs to proprietary domains. However, retrieved documents may contain sensitive knowledge, posing risks o…
MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language Models
Yujing Wang, Hainan Zhang, Liang Pang +3
In a real-world RAG system, the current query often involves spoken ellipses and ambiguous references from dialogue contexts, necessitating query rewriting to better describe user'…
Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning
Yujing Wang, Hainan Zhang, Sijia Wen +2
Federated learning is highly susceptible to model poisoning attacks, especially those meticulously crafted for servers. Traditional defense methods mainly focus on updating assessm…