3 citations · 4 across the 3 of their papers we have counts for
5 papers · 1 filter
Denoising Iterative Self-Correction: Structured Verification Loops for Reliable LLM Reasoning
Shen Yin, David Ken, Joel Stremmel
Large language models produce fluent but often incorrect multi-step reasoning, and naive correction methods risk degrading already-correct answers. We introduce Denoising Iterative…
Give me Some Hard Questions: Synthetic Data Generation for Clinical QA
Fan Bai, Keith Harrigian, Joel Stremmel +3
Clinical Question Answering (QA) systems enable doctors to quickly access patient information from electronic health records (EHRs). However, training these systems requires signif…
LLMs in Biomedicine: A study on clinical Named Entity Recognition
Masoud Monajatipoor, Jiaxin Yang, Joel Stremmel +4
Large Language Models (LLMs) demonstrate remarkable versatility in various NLP tasks but encounter distinct challenges in biomedical due to the complexities of language and data sc…
XAIQA: Explainer-Based Data Augmentation for Extractive Question Answering
Joel Stremmel, Ardavan Saeedi, Hamid Hassanzadeh +4
Extractive question answering (QA) systems can enable physicians and researchers to query medical records, a foundational capability for designing clinical studies and understandin…
Surpassing GPT-4 Medical Coding with a Two-Stage Approach
Zhichao Yang, Sanjit Singh Batra, Joel Stremmel +1
Recent advances in large language models (LLMs) show potential for clinical applications, such as clinical decision support and trial recommendations. However, the GPT-4 LLM predic…