Larger batch sizes for LLM dialogue coding in healthcare simulations improve speed and reduce energy consumption while decreasing coding accuracy compared to human labels.
In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
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Self-prompting combined with QLoRA and DPO on small open-weight models yields micro F1 scores up to 0.864 on clinical named entity recognition from 1,200 dental notes.
citing papers explorer
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Scalable LLM-based Coding of Dialogue in Healthcare Simulation: Balancing Coding Performance, Processing Time, and Environmental Impact
Larger batch sizes for LLM dialogue coding in healthcare simulations improve speed and reduce energy consumption while decreasing coding accuracy compared to human labels.
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Self-Prompting Small Language Models for Privacy-Sensitive Clinical Information Extraction
Self-prompting combined with QLoRA and DPO on small open-weight models yields micro F1 scores up to 0.864 on clinical named entity recognition from 1,200 dental notes.