Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Wang, Junda"'
Large language models (LLMs) have shown remarkable capabilities in various natural language processing tasks, yet they often struggle with maintaining factual accuracy, particularly in knowledge-intensive domains like healthcare. This study introduce
Externí odkaz:
http://arxiv.org/abs/2410.23526
Autor:
Wang, Junda, Ting, Yujan, Chen, Eric Z., Tran, Hieu, Yu, Hong, Huang, Weijing, Chen, Terrence
Multimodal large language models (MLLMs) have made significant strides, yet they face challenges in the medical domain due to limited specialized knowledge. While recent medical MLLMs demonstrate strong performance in lab settings, they often struggl
Externí odkaz:
http://arxiv.org/abs/2410.14948
Autor:
Yao, Zonghai, Zhang, Zihao, Tang, Chaolong, Bian, Xingyu, Zhao, Youxia, Yang, Zhichao, Wang, Junda, Zhou, Huixue, Jang, Won Seok, Ouyang, Feiyun, Yu, Hong
Artificial intelligence (AI) and large language models (LLMs) in healthcare require advanced clinical skills (CS), yet current benchmarks fail to evaluate these comprehensively. We introduce MedQA-CS, an AI-SCE framework inspired by medical education
Externí odkaz:
http://arxiv.org/abs/2410.01553
Autor:
Vashisht, Parth, Lodha, Abhilasha, Maddipatla, Mukta, Yao, Zonghai, Mitra, Avijit, Yang, Zhichao, Wang, Junda, Kwon, Sunjae, Yu, Hong
This paper presents our team's participation in the MEDIQA-ClinicalNLP2024 shared task B. We present a novel approach to diagnosing clinical dermatology cases by integrating large multimodal models, specifically leveraging the capabilities of GPT-4V
Externí odkaz:
http://arxiv.org/abs/2404.17749
Autor:
Zhang, Zhe, Guan, Yifei, Wang, Junda, Apffel, Benjamin, Bossart, Aleksi, Qin, Haoye, Yazyev, Oleg V., Fleury, Romain
Exploring and understanding topological phases in systems with strong distributed disorder requires developing fundamentally new approaches to replace traditional tools such as topological band theory. Here, we present a general real-space renormaliz
Externí odkaz:
http://arxiv.org/abs/2404.15866
Large Language Models (LLMs) have demonstrated a remarkable potential in medical knowledge acquisition and question-answering. However, LLMs can potentially hallucinate and yield factually incorrect outcomes, even with domain-specific pretraining. Pr
Externí odkaz:
http://arxiv.org/abs/2402.17887
Autor:
Wang, Junda, Yao, Zonghai, Yang, Zhichao, Zhou, Huixue, Li, Rumeng, Wang, Xun, Xu, Yucheng, Yu, Hong
We introduce NoteChat, a novel cooperative multi-agent framework leveraging Large Language Models (LLMs) to generate patient-physician dialogues. NoteChat embodies the principle that an ensemble of role-specific LLMs, through structured role-play and
Externí odkaz:
http://arxiv.org/abs/2310.15959
Using high-frequency donation records from a major medical crowdfunding site and careful difference-in-difference analysis, we demonstrate that the 2020 BLM surge decreased the fundraising gap between Black and non-Black beneficiaries by around 50\%.
Externí odkaz:
http://arxiv.org/abs/2310.14590
This paper presents UMASS_BioNLP team participation in the MEDIQA-Chat 2023 shared task for Task-A and Task-C. We focus especially on Task-C and propose a novel LLMs cooperation system named a doctor-patient loop to generate high-quality conversation
Externí odkaz:
http://arxiv.org/abs/2306.16931
Autor:
Wang, Junda, Li, Weijian, Wang, Han, Lyu, Hanjia, Thirukumaran, Caroline, Mesfin, Addisu, Luo, Jiebo
Causal inference and model interpretability research are gaining increasing attention, especially in the domains of healthcare and bioinformatics. Despite recent successes in this field, decorrelating features under nonlinear environments with human
Externí odkaz:
http://arxiv.org/abs/2209.14975