7 papers
MedFabric and EtHER: A Data-Centric Framework for Word-Level Fabrication Generation and Detection in Medical LLMs
Tung Sum Thomas Kwok, Qian Qian, Xiaofeng Lin +8
Large Language Models exhibit strong reasoning and semantic understanding capabilities but often hallucinate in domains that require expert knowledge, among which fabrications, the…
MedicalBench: Evaluating Large Language Models Toward Improved Medical Concept Extraction
Zhichao Yang, Gregory D. Lyng, Sanjit Singh Batra +1
Medical concept extraction from electronic health records underpins many downstream applications, yet remains challenging because medically meaningful concepts are frequently impli…
Imputation of Unknown Missingness in Sparse Electronic Health Records
Jun Han, Josue Nassar, Sanjit Singh Batra +3
Machine learning holds great promise for advancing the field of medicine, with electronic health records (EHRs) serving as a primary data source. However, EHRs are often sparse and…
Fast and Effective On-policy Distillation from Reasoning Prefixes
Dongxu Zhang, Zhichao Yang, Sepehr Janghorbani +6
On-policy distillation (OPD), which samples trajectories from the student model and supervises them with a teacher at the token level, avoids relying solely on verifiable terminal…
ESTAR: Early-Stopping Token-Aware Reasoning For Efficient Inference
Junda Wang, Zhichao Yang, Dongxu Zhang +2
Large reasoning models (LRMs) achieve state-of-the-art performance by generating long chains-of-thought, but often waste computation on redundant reasoning after the correct answer…
POET: Protocol Optimization via Eligibility Tuning
Trisha Das, Katherine Kero, Dorinda Schumann +4
Eligibility criteria (EC) are essential for clinical trial design, yet drafting them remains a time-intensive and cognitively demanding task for clinicians. Existing automated appr…