253 citations · 415 across the 23 of their papers we have counts for
10 papers · 1 filter
HERO: Harnessing Evidence for Test-time Refinement and Prompt Optimization in Program Generation
Mingyu Lu, Timothy Ossowski, Tristan Naumann +2
Harness optimization improves LLMs by refining reusable prompts and scaffolding, yet requires further adaptation for program generation tasks such as text-to-SQL and text-to-Python…
Large Language Models are Approximate Survival Estimators
Juan M Zambrano Chaves, Peniel Argaw, Risa Ueno +5
Survival analysis estimates time-to-event outcomes from patient covariates and is widely used for medical risk assessment. Patients seeking prognostic information after a diagnosis…
Be My Eyes: Extending Large Language Models to New Modalities Through Multi-Agent Collaboration
James Y. Huang, Sheng Zhang, Qianchu Liu +5
Large Language Models (LLMs) have demonstrated remarkable capabilities in challenging, knowledge-intensive reasoning tasks. However, extending LLMs to perceive and reason over a ne…
BioClinical ModernBERT: A State-of-the-Art Long-Context Encoder for Biomedical and Clinical NLP
Thomas Sounack, Joshua Davis, Brigitte Durieux +7
Encoder-based transformer models are central to biomedical and clinical Natural Language Processing (NLP), as their bidirectional self-attention makes them well-suited for efficien…
Exploring Scaling Laws for EHR Foundation Models
Sheng Zhang, Qin Liu, Naoto Usuyama +3
The emergence of scaling laws has profoundly shaped the development of large language models (LLMs), enabling predictable performance gains through systematic increases in model si…
Med-RLVR: Emerging Medical Reasoning from a 3B base model via reinforcement Learning
Sheng Zhang, Qianchu Liu, Guanghui Qin +2
Reinforcement learning from verifiable rewards (RLVR) has recently gained attention for its ability to elicit self-evolved reasoning capabilitie from base language models without e…
Universal Abstraction: Harnessing Frontier Models to Structure Real-World Data at Scale
Cliff Wong, Sam Preston, Qianchu Liu +22
A significant fraction of real-world patient information resides in unstructured clinical text. Medical abstraction extracts and normalizes key structured attributes from free-text…
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…
DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generation
Yiqing Xie, Sheng Zhang, Hao Cheng +6
Medical text generation aims to assist with administrative work and highlight salient information to support decision-making. To reflect the specific requirements of medical text,…