11 citations · 34 across the 21 of their papers we have counts for
8 papers · 1 filter
Foundation Agents Meet Agentic Deep Research: Evidence-Grounded Clinical Code Forecasting
Junda Wang, Meysam Ghaffari, Akshat Choube +3
Next-encounter ICD forecasting predicts which standardized diagnosis codes will be documented at a future visit from the longitudinal record available beforehand. The task is prosp…
From Scores to Steps: Diagnosing and Improving LLM Performance in Evidence-Based Medical Calculations
Benlu Wang, Iris Xia, Yifan Zhang +6
Large language models (LLMs) have demonstrated promising performance on medical benchmarks; however, their ability to perform medical calculations, a crucial aspect of clinical dec…
RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models
Hieu Tran, Zonghai Yao, Junda Wang +3
This work introduces RARE (Retrieval-Augmented Reasoning Enhancement), a versatile extension to the mutual reasoning framework (rStar), aimed at enhancing reasoning accuracy and fa…
LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models
Hieu Tran, Junda Wang, Yujan Ting +2
Large language models (LLMs) have shown remarkable capabilities in various natural language processing tasks, yet they often struggle with maintaining factual accuracy, particularl…
MultiViewDx: Evidence-Linked Multi-View Clinical Diagnosis
Junda Wang, Yujan Ting, Zonghai Yao +5
Medical multimodal large language models (MLLMs) can perform well on existing medical visual question answering (MedVQA) benchmarks, but their training data often does not match cl…
JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capability
Junda Wang, Zhichao Yang, Zonghai Yao +1
Large Language Models (LLMs) have demonstrated a remarkable potential in medical knowledge acquisition and question-answering. However, LLMs can potentially hallucinate and yield f…
NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes
Junda Wang, Zonghai Yao, Zhichao Yang +5
We introduce NoteChat, a novel cooperative multi-agent framework leveraging Large Language Models (LLMs) to generate patient-physician dialogues. NoteChat embodies the principle th…
UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?
Junda Wang, Zonghai Yao, Avijit Mitra +3
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 sy…