23 citations · 48 across the 20 of their papers we have counts for
23 papers · 1 filter
A Multi-granularity Interaction Network for Relational Triple Extraction
Xiaocheng Luo, Yanping Chen, Zaifu Zhan
Relational triple extraction identifies entity relations from raw sentences without depending on entity type information. The performance of traditional methods is seriously degene…
CrisisFake: Benchmark Validity of AI-Generated Text Detection for Disaster Social Sensing
Xiaoshan Zhou, Zaifu Zhan
Disaster social sensing converts public social-media posts into evidence for situational awareness and humanitarian response, but plausible LLM-generated posts can contaminate this…
ChestPheNoT: Deployable, Auditable Label-Status-Evidence Extraction from Radiology Reports
Kai Yu, Chenyu Zhu, Zaifu Zhan +5
Structured phenotype extraction from radiology reports supports cohort construction, quality auditing, and clinical analytics, but practical deployment requires local inference and…
Let Large Language Models Judge Each Other: Multi-Agent Peer-Reviewed Reasoning for Medical Question Answering
Zaifu Zhan, Shuang Zhou, Rui Zhang
Objective: To enhance the accuracy, interpretability, and robustness of large language models (LLMs) in medical question answering (MedQA). Method: We designed a multi-agent peer-r…
RuPLaR : Efficient Latent Compression of LLM Reasoning Chains with Rule-Based Priors From Multi-Step to One-Step
Xiaocheng Luo, Kang Wang, Zaifu Zhan +2
The Chain-of-Thought (CoT) paradigm, while enhancing the interpretability of Large Language Models (LLMs), is constrained by the inefficiencies and expressive limits of natural lan…
Can Large Language Models Self-Correct in Medical Question Answering? An Exploratory Study
Zaifu Zhan, Mengyuan Cui, Rui Zhang
Large language models (LLMs) have achieved strong performance on medical question answering (medical QA), and chain-of-thought (CoT) prompting has further improved results by elici…
An Underexplored Frontier: Large Language Models for Rare Disease Patient Education and Communication -- A scoping review
Zaifu Zhan, Yu Hou, Kai Yu +4
Rare diseases affect over 300 million people worldwide and are characterized by complex care pathways, limited clinical expertise, and substantial unmet communication needs through…
EpiScreen: Early Epilepsy Detection from Electronic Health Records with Large Language Models
Shuang Zhou, Kai Yu, Zaifu Zhan +5
Epilepsy and psychogenic non-epileptic seizures often present with similar seizure-like manifestations but require fundamentally different management strategies. Misdiagnosis is co…
HeartAgent: An Autonomous Agent System for Explainable Differential Diagnosis in Cardiology
Shuang Zhou, Kai Yu, Song Wang +11
Heart diseases remain a leading cause of morbidity and mortality worldwide, necessitating accurate and trustworthy differential diagnosis. However, existing artificial intelligence…
To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering
Zaifu Zhan, Min Zeng, Shuang Zhou +6
Objective: To improve the efficiency of medical question answering (MedQA) with large language models (LLMs) by avoiding unnecessary reasoning while maintaining accuracy. Methods:…
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…
Automating Expert-Level Medical Reasoning Evaluation of Large Language Models
Shuang Zhou, Wenya Xie, Jiaxi Li +16
As large language models (LLMs) become increasingly integrated into clinical decision-making, ensuring transparent and trustworthy reasoning is essential. However, existing evaluat…
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…
An evaluation of DeepSeek Models in Biomedical Natural Language Processing
Zaifu Zhan, Shuang Zhou, Huixue Zhou +4
The advancement of Large Language Models (LLMs) has significantly impacted biomedical Natural Language Processing (NLP), enhancing tasks such as named entity recognition, relation…
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…
PaperHelper: Knowledge-Based LLM QA Paper Reading Assistant
Congrui Yin, Evan Wei, Zhongxing Zhang +1
In the paper, we introduce a paper reading assistant, PaperHelper, a potent tool designed to enhance the capabilities of researchers in efficiently browsing and understanding scien…
Towards Better Multi-task Learning: A Framework for Optimizing Dataset Combinations in Large Language Models
Zaifu Zhan, Rui Zhang
To efficiently select optimal dataset combinations for enhancing multi-task learning (MTL) performance in large language models, we proposed a novel framework that leverages a neur…
RAMIE: Retrieval-Augmented Multi-task Information Extraction with Large Language Models on Dietary Supplements
Zaifu Zhan, Shuang Zhou, Mingchen Li +1
\textbf{Objective:} We aimed to develop an advanced multi-task large language model (LLM) framework to extract multiple types of information about dietary supplements (DS) from cli…
Large Language Models for Disease Diagnosis: A Scoping Review
Shuang Zhou, Zidu Xu, Mian Zhang +14
Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intellige…
CancerLLM: A Large Language Model in Cancer Domain
Mingchen Li, Jiatan Huang, Jeremy Yeung +5
Medical Large Language Models (LLMs) have demonstrated impressive performance on a wide variety of medical NLP tasks; however, there still lacks a LLM specifically designed for phe…
Benchmarking Retrieval-Augmented Large Language Models in Biomedical NLP: Application, Robustness, and Self-Awareness
Mingchen Li, Zaifu Zhan, Han Yang +3
Large language models (LLM) have demonstrated remarkable capabilities in various biomedical natural language processing (NLP) tasks, leveraging the demonstration within the input c…