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cs.CL2025
A Systematic Analysis of Large Language Models with RAG-enabled Dynamic Prompting for Medical Error Detection and Correction
Farzad Ahmed, Joniel Augustine Jerome, Meliha Yetisgen +1
Objective: Clinical documentation contains factual, diagnostic, and management errors that can compromise patient safety. Large language models (LLMs) may help detect and correct s…
cs.CL2025
Does Data Contamination Detection Work (Well) for LLMs? A Survey and Evaluation on Detection Assumptions
Yujuan Fu, Ozlem Uzuner, Meliha Yetisgen +1
Large language models (LLMs) have demonstrated great performance across various benchmarks, showing potential as general-purpose task solvers. However, as LLMs are typically traine…
cs.CL2024
CACER: Clinical Concept Annotations for Cancer Events and Relations
Yujuan Fu, Giridhar Kaushik Ramachandran, Ahmad Halwani +5
Clinical notes contain unstructured representations of patient histories, including the relationships between medical problems and prescription drugs. To investigate the relationsh…