Larger batch sizes for LLM dialogue coding in healthcare simulations improve speed and reduce energy consumption while decreasing coding accuracy compared to human labels.
JMIR Medical Informatics12, e55318 (Apr 2024)
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SPEAR proposes structured prompt views, runtime adaptive refinement, and policy rules to make prompts first-class, versioned, and evolvable components in complex LLM applications.
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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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Making Prompts First-Class Citizens for Adaptive LLM Pipelines
SPEAR proposes structured prompt views, runtime adaptive refinement, and policy rules to make prompts first-class, versioned, and evolvable components in complex LLM applications.