2 citations · 3 across the 7 of their papers we have counts for
6 papers · 1 filter
Benchmarking GPT-5 for biomedical natural language processing
Yu Hou, Zaifu Zhan, Min Zeng +3
Biomedical literature and clinical narratives pose multifaceted challenges for natural language understanding, from precise entity extraction and document synthesis to multi-step d…
Quantized Large Language Models in Biomedical Natural Language Processing: Evaluation and Recommendation
Zaifu Zhan, Shuang Zhou, Min Zeng +6
Large language models have demonstrated remarkable capabilities in biomedical natural language processing, yet their rapid growth in size and computational requirements present a m…
Data-Efficient Biomedical In-Context Learning: A Diversity-Enhanced Submodular Perspective
Jun Wang, Zaifu Zhan, Qixin Zhang +3
Recent progress in large language models (LLMs) has leveraged their in-context learning (ICL) abilities to enable quick adaptation to unseen biomedical NLP tasks. By incorporating…
Uncertainty-Aware Large Language Models for Explainable Disease Diagnosis
Shuang Zhou, Jiashuo Wang, Zidu Xu +11
Explainable disease diagnosis, which leverages patient information (e.g., signs and symptoms) and computational models to generate probable diagnoses and reasonings, offers clear c…
Retrieval-augmented in-context learning for multimodal large language models in disease classification
Zaifu Zhan, Shuang Zhou, Xiaoshan Zhou +6
Objectives: We aim to dynamically retrieve informative demonstrations, enhancing in-context learning in multimodal large language models (MLLMs) for disease classification. Methods…
MMRAG: Multi-Mode Retrieval-Augmented Generation with Large Language Models for Biomedical In-Context Learning
Zaifu Zhan, Jun Wang, Shuang Zhou +2
Objective: To optimize in-context learning in biomedical natural language processing by improving example selection. Methods: We introduce a novel multi-mode retrieval-augmented ge…