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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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.