1 citations · 1 across the 3 of their papers we have counts for
4 papers
CLEAR: Cross-Source Evidence Adjudication for Large Language Models in Medicine
Shuai Wang, Yize Zhao, Qingyu Chen
Medical knowledge evolves continuously, whereas the parametric knowledge encoded in large language models (LLMs) is fixed at training time. External retrieval, including retrieval-…
Toward Federated Large Language Models in Medicine: A Parameter-Efficient Framework for Privacy-Preserving, Multi-Institutional Adaptation
Anran Li, Yuanyuan Chen, Wenjun Long +16
Large language models (LLMs) are increasingly adapted for medical applications, but most are trained using data from a single institution because privacy and governance constraints…
Enhancing Patient-Centric Communication: Leveraging LLMs to Simulate Patient Perspectives
Xinyao Ma, Rui Zhu, Zihao Wang +5
Large Language Models (LLMs) have demonstrated impressive capabilities in role-playing scenarios, particularly in simulating domain-specific experts using tailored prompts. This ab…
Federated Graph Learning with Adaptive Importance-based Sampling
Anran Li, Yuanyuan Chen, Chao Ren +5
For privacy-preserving graph learning tasks involving distributed graph datasets, federated learning (FL)-based GCN (FedGCN) training is required. A key challenge for FedGCN is sca…