collaborators

8 papers

cs.CL2026

Retrieval-Augmented LLMs for Evidence Localization in Clinical Trial Recruitment from Longitudinal EHR Narratives

Ziyi Chen, Mengxian Lyu, Cheng Peng +1

Screening patients for enrollment is a well-known, labor-intensive bottleneck that leads to under-enrollment and, ultimately, trial failures. Recent breakthroughs in large language…

cs.CL2026

Detecting HIV-Related Stigma in Clinical Narratives Using Large Language Models

Ziyi Chen, Yasir Khan, Mengyuan Zhang +7

Human immunodeficiency virus (HIV)-related stigma is a critical psychosocial determinant of health for people living with HIV (PLWH), influencing mental health, engagement in care,…

cs.CL2026

Improving Automatic Summarization of Radiology Reports through Mid-Training of Large Language Models

Mengxian Lyu, Cheng Peng, Ziyi Chen +3

Automatic summarization of radiology reports is an essential application to reduce the burden on physicians. Previous studies have widely used the "pre-training, fine-tuning" strat…

cs.CL2026

A Parameter-Efficient Transfer Learning Approach through Multitask Prompt Distillation and Decomposition for Clinical NLP

Cheng Peng, Mengxian Lyu, Ziyi Chen +1

Existing prompt-based fine-tuning methods typically learn task-specific prompts independently, imposing significant computing and storage overhead at scale when deploying multiple…

cs.CL2026

Countering Catastrophic Forgetting of Large Language Models for Better Instruction Following via Weight-Space Model Merging

Mengxian Lyu, Cheng Peng, Ziyi Chen +3

Large language models have been adopted in the medical domain for clinical documentation to reduce clinician burden. However, studies have reported that LLMs often "forget" a signi…

cs.CL2026

MedGPT-oss: Training a General-Purpose Vision-Language Model for Biomedicine

Kai Zhang, Zhengqing Yuan, Cheng Peng +10

Biomedical multimodal assistants have the potential to unify radiology, pathology, and clinical-text reasoning, yet a critical deployment gap remains: top-performing systems are ei…