400 citations · 513 across the 22 of their papers we have counts for
24 papers
Exploring Recurrent-Depth Language Models for Multitask Clinical Text Processing
Xi Yang, Guodong Liu, Cheng Peng +7
Clinical natural language processing includes diverse tasks such as information extraction, classification, and question answering. Developing one model for all tasks could simplif…
An Agentic Generative Large Language Model for Treatment Planning of Colorectal Cancer
Mengxian Lyu, Cheng Peng, Tim Jang +18
Treatment planning in precision oncology requires synthesizing heterogeneous patient information with rapidly evolving clinical guidelines to ensure guideline-concordant care. Whil…
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…
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…
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…
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…
A Structure-Agnostic Co-Tuning Framework for LLMs and SLMs in Cloud-Edge Systems
Yuze Liu, Yunhan Wang, Tiehua Zhang +5
The surge in intelligent applications driven by large language models (LLMs) has made it increasingly difficult for bandwidth-limited cloud servers to process extensive LLM workloa…
A Study of Large Language Models for Patient Information Extraction: Model Architecture, Fine-Tuning Strategy, and Multi-task Instruction Tuning
Cheng Peng, Xinyu Dong, Mengxian Lyu +3
Natural language processing (NLP) is a key technology to extract important patient information from clinical narratives to support healthcare applications. The rapid development of…
Scaling Up Biomedical Vision-Language Models: Fine-Tuning, Instruction Tuning, and Multi-Modal Learning
Cheng Peng, Kai Zhang, Mengxian Lyu +3
To advance biomedical vison-language model capabilities through scaling up, fine-tuning, and instruction tuning, develop vision-language models with improved performance in handlin…
AGENT-X: Adaptive Guideline-based Expert Network for Threshold-free AI-generated teXt detection
Jiatao Li, Mao Ye, Cheng Peng +2
Existing AI-generated text detection methods heavily depend on large annotated datasets and external threshold tuning, restricting interpretability, adaptability, and zero-shot eff…
Natural Language Generation in Healthcare: A Review of Methods and Applications
Mengxian Lyu, Xiaohan Li, Ziyi Chen +4
Natural language generation (NLG) is the key technology to achieve generative artificial intelligence (AI). With the breakthroughs in large language models (LLMs), NLG has been wid…
Harnessing Negative Signals: Reinforcement Distillation from Teacher Data for LLM Reasoning
Shuyao Xu, Cheng Peng, Jiangxuan Long +3
Recent advances in model distillation show that data from advanced reasoning models can effectively train smaller student models. However, standard practices discard incorrect reas…
OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs
Jintian Zhang, Cheng Peng, Mengshu Sun +6
Despite the recent advancements in Large Language Models (LLMs), which have significantly enhanced the generative capabilities for various NLP tasks, LLMs still face limitations in…
UF-HOBI at "Discharge Me!": A Hybrid Solution for Discharge Summary Generation Through Prompt-based Tuning of GatorTronGPT Models
Mengxian Lyu, Cheng Peng, Daniel Paredes +4
Automatic generation of discharge summaries presents significant challenges due to the length of clinical documentation, the dispersed nature of patient information, and the divers…
AutoScraper: A Progressive Understanding Web Agent for Web Scraper Generation
Wenhao Huang, Zhouhong Gu, Chenghao Peng +5
Web scraping is a powerful technique that extracts data from websites, enabling automated data collection, enhancing data analysis capabilities, and minimizing manual data entry ef…
Improving Generalizability of Extracting Social Determinants of Health Using Large Language Models through Prompt-tuning
Cheng Peng, Zehao Yu, Kaleb E Smith +3
The progress in natural language processing (NLP) using large language models (LLMs) has greatly improved patient information extraction from clinical narratives. However, most met…
Automatic Summarization of Doctor-Patient Encounter Dialogues Using Large Language Model through Prompt Tuning
Mengxian Lyu, Cheng Peng, Xiaohan Li +3
Automatic text summarization (ATS) is an emerging technology to assist clinicians in providing continuous and coordinated care. This study presents an approach to summarize doctor-…
NewsBench: A Systematic Evaluation Framework for Assessing Editorial Capabilities of Large Language Models in Chinese Journalism
Miao Li, Ming-Bin Chen, Bo Tang +8
We present NewsBench, a novel evaluation framework to systematically assess the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism. Our c…
Me LLaMA: Foundation Large Language Models for Medical Applications
Qianqian Xie, Qingyu Chen, Aokun Chen +15
Recent advancements in large language models (LLMs) like ChatGPT and LLaMA show promise in medical applications, yet challenges remain in medical language comprehension. This study…
Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need
Cheng Peng, Xi Yang, Aokun Chen +6
Objective To solve major clinical natural language processing (NLP) tasks using a unified text-to-text learning architecture based on a generative large language model (LLM) via pr…
On the Impact of Cross-Domain Data on German Language Models
Amin Dada, Aokun Chen, Cheng Peng +12
Traditionally, large language models have been either trained on general web crawls or domain-specific data. However, recent successes of generative large language models, have she…
Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction
Cheng Peng, Xi Yang, Kaleb E Smith +4
Objective To develop soft prompt-based learning algorithms for large language models (LLMs), examine the shape of prompts, prompt-tuning using frozen/unfrozen LLMs, transfer learni…
A Study of Generative Large Language Model for Medical Research and Healthcare
Cheng Peng, Xi Yang, Aokun Chen +16
There is enormous enthusiasm and concerns in using large language models (LLMs) in healthcare, yet current assumptions are all based on general-purpose LLMs such as ChatGPT. This s…
Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension
Cheng Peng, Xi Yang, Zehao Yu +3
Objective: To develop a natural language processing system that solves both clinical concept extraction and relation extraction in a unified prompt-based machine reading comprehens…