2 citations · 2 across the 6 of their papers we have counts for
6 papers · 1 filter
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…
SemiHVision: Enhancing Medical Multimodal Models with a Semi-Human Annotated Dataset and Fine-Tuned Instruction Generation
Junda Wang, Yujan Ting, Eric Z. Chen +4
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 ML…
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…