Low information density is identified as the root cause of NER failures on user-generated content, with the Window-Aware Optimization Module delivering up to 4.5% F1 gains and new SOTA on WNUT2017.
LLMs in biomedicine: a study on clinical named entity recognition
5 Pith papers cite this work. Polarity classification is still indexing.
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Physicians use substantially more risk-focused framing in counseling notes for repeat cesarean than for VBAC among patients clinically eligible for both.
Decomposing annotation tasks using centers from centering theory reduces aggregate inferential load via a degrees-of-freedom model and enables better sub-task allocation.
Few-shot prompting with GPT-4.1 achieves an F1 score of 0.65 for extracting and classifying substance use mentions in Spanish clinical texts as part of the ToxHabits shared task.
citing papers explorer
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A Mechanism and Optimization Study on the Impact of Information Density on User-Generated Content Named Entity Recognition
Low information density is identified as the root cause of NER failures on user-generated content, with the Window-Aware Optimization Module delivering up to 4.5% F1 gains and new SOTA on WNUT2017.
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Implicit Framing in Obstetric Counseling Notes: A Grounded LLM Pipeline on a VBAC-Eligible Cohort
Physicians use substantially more risk-focused framing in counseling notes for repeat cesarean than for VBAC among patients clinically eligible for both.
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Task Decomposition for Efficient Annotation
Decomposing annotation tasks using centers from centering theory reduces aggregate inferential load via a degrees-of-freedom model and enables better sub-task allocation.
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FMI@SU ToxHabits: Evaluating LLMs Performance on Toxic Habit Extraction in Spanish Clinical Texts
Few-shot prompting with GPT-4.1 achieves an F1 score of 0.65 for extracting and classifying substance use mentions in Spanish clinical texts as part of the ToxHabits shared task.
- Enhancing LLMs for Identifying and Prioritizing Important Medical Jargons from Electronic Health Record Notes Utilizing Data Augmentation: A Comparative Study