68 citations · 82 across the 10 of their papers we have counts for
10 papers
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…
Memorization in Large Language Models in Medicine: Prevalence, Characteristics, and Implications
Anran Li, Lingfei Qian, Mengmeng Du +18
Large Language Models (LLMs) have demonstrated significant potential in medicine, with many studies adapting them through continued pre-training or fine-tuning on medical data to e…
Multi-Label Classification with Generative AI Models in Healthcare: A Case Study of Suicidality and Risk Factors
Ming Huang, Zehan Li, Yan Hu +9
Suicide remains a pressing global health crisis, with over 720,000 deaths annually and millions more affected by suicide ideation (SI) and suicide attempts (SA). Early identificati…
Unveiling Large Language Model Supply Chain: Structure, Domain, and Vulnerabilities
Yanzhe Hu, Shenao Wang, Tianyuan Nie +2
Large Language Models (LLMs) have revolutionized artificial intelligence (AI), driving breakthroughs in natural language understanding, text generation, and autonomous systems. How…
CDEMapper: Enhancing NIH Common Data Element Normalization using Large Language Models
Yan Wang, Jimin Huang, Huan He +13
Common Data Elements (CDEs) standardize data collection and sharing across studies, enhancing data interoperability and improving research reproducibility. However, implementing CD…
BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devices
Xudong Lu, Yinghao Chen, Cheng Chen +19
The emergence and growing popularity of multimodal large language models (MLLMs) have significant potential to enhance various aspects of daily life, from improving communication t…
Information Extraction from Clinical Notes: Are We Ready to Switch to Large Language Models?
Yan Hu, Xu Zuo, Yujia Zhou +9
Backgrounds: Information extraction (IE) is critical in clinical natural language processing (NLP). While large language models (LLMs) excel on generative tasks, their performance…
Suicide Phenotyping from Clinical Notes in Safety-Net Psychiatric Hospital Using Multi-Label Classification with Pre-Trained Language Models
Zehan Li, Yan Hu, Scott Lane +7
Accurate identification and categorization of suicidal events can yield better suicide precautions, reducing operational burden, and improving care quality in high-acuity psychiatr…
UCFE: A User-Centric Financial Expertise Benchmark for Large Language Models
Yuzhe Yang, Yifei Zhang, Yan Hu +10
This paper introduces the UCFE: User-Centric Financial Expertise benchmark, an innovative framework designed to evaluate the ability of large language models (LLMs) to handle compl…
Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering
Yan Hu, Qingyu Chen, Jingcheng Du +9
Objective: This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their perform…