7 papers
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…