1.2k citations · 2.1k across the 41 of their papers we have counts for
10 papers · 1 filter
From Medprompt to o1: Exploration of Run-Time Strategies for Medical Challenge Problems and Beyond
Harsha Nori, Naoto Usuyama, Nicholas King +4
Run-time steering strategies like Medprompt are valuable for guiding large language models (LLMs) to top performance on challenging tasks. Medprompt demonstrates that a general LLM…
MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imaging
Noel C. F. Codella, Ying Jin, Shrey Jain +28
In this work, we present MedImageInsight, an open-source medical imaging embedding model. MedImageInsight is trained on medical images with associated text and labels across a dive…
MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding
Fei Wang, Xingyu Fu, James Y. Huang +18
We introduce MuirBench, a comprehensive benchmark that focuses on robust multi-image understanding capabilities of multimodal LLMs. MuirBench consists of 12 diverse multi-image tas…
From Introspection to Best Practices: Principled Analysis of Demonstrations in Multimodal In-Context Learning
Nan Xu, Fei Wang, Sheng Zhang +2
Motivated by in-context learning (ICL) capabilities of Large Language Models (LLMs), multimodal LLMs with additional visual modality are also exhibited with similar ICL abilities w…
mDPO: Conditional Preference Optimization for Multimodal Large Language Models
Fei Wang, Wenxuan Zhou, James Y. Huang +4
Direct preference optimization (DPO) has shown to be an effective method for large language model (LLM) alignment. Recent works have attempted to apply DPO to multimodal scenarios…
MedFuzz: Exploring the Robustness of Large Language Models in Medical Question Answering
Robert Osazuwa Ness, Katie Matton, Hayden Helm +4
Large language models (LLM) have achieved impressive performance on medical question-answering benchmarks. However, high benchmark accuracy does not imply that the performance gene…
Offset Unlearning for Large Language Models
James Y. Huang, Wenxuan Zhou, Fei Wang +4
Despite the strong capabilities of Large Language Models (LLMs) to acquire knowledge from their training corpora, the memorization of sensitive information in the corpora such as c…
Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation
Juan Manuel Zambrano Chaves, Shih-Cheng Huang, Yanbo Xu +24
The scaling laws and extraordinary performance of large foundation models motivate the development and utilization of such models in biomedicine. However, despite early promising r…
Attribute Structuring Improves LLM-Based Evaluation of Clinical Text Summaries
Zelalem Gero, Chandan Singh, Yiqing Xie +6
Summarizing clinical text is crucial in health decision-support and clinical research. Large language models (LLMs) have shown the potential to generate accurate clinical text summ…
T-Rex: Text-assisted Retrosynthesis Prediction
Yifeng Liu, Hanwen Xu, Tangqi Fang +5
As a fundamental task in computational chemistry, retrosynthesis prediction aims to identify a set of reactants to synthesize a target molecule. Existing template-free approaches o…