Core Concepts
Automatic Prompt Optimization (APO) enhances prompt quality, leading to improved clinical note generation with expert customization.
Abstract
The study explores the impact of prompt engineering on Large Language Models (LLMs) in clinical note generation. It introduces an Automatic Prompt Optimization (APO) framework to refine prompts and compares outputs of medical experts, non-medical experts, and APO-enhanced LLMs. Results show superior performance of APO-GPT4 in standardizing prompt quality. Expert customization post-APO maintains content quality, emphasizing a two-phase optimization process leveraging APO-GPT4 and expert input.
Introduction
Large Language Models (LLMs) expand natural language processing applications.
Quality prompts are crucial for guiding LLMs in document generation.
Human expression complexities challenge prompt creation for LLMs.
Variability in prompt quality affects LLM performance consistency.
Method
Algorithm details SOAP Note Prompt Optimization.
Forward pass uses generic prompts to generate summaries.
Backward pass refines prompts based on generated summaries.
Human-in-the-loop component involves expert modifications post-APO.
Experiments
Comparative analysis shows APO-GPT4 outperforms other methods.
Human interventions post-APO maintain high standards set by APO.
Expert preference favors personalized tweaks without compromising content integrity.
Conclusion
Prompt engineering significantly impacts LLM effectiveness in clinical note generation.
A two-pronged approach using APO-GPT4 and expert customization is recommended for optimal results.
Stats
Results highlight GPT4-APO’s superior performance across clinical note sections.
Quotes
"Variances in prompt quality lead to differences in prompt efficacy."
"A two-phase optimization process is recommended for consistency and personalization."