Comparing LLM and Fine-Tuned Model Performance on NVDRS Circumstance Extraction with Varying Prompt Complexity
arXiv:2605.21845v1 Announce Type: new Abstract: Suicide is a leading cause of death in the United States, and understanding the circumstances that precede it requires extracting structured information from death investigation narratives. Many of these circumstances require semantic inference beyond simple keyword matching. We develop a ``Complexity Score'' algorithm that analyzes coding manual structure to predict when detailed prompts with full coding guidelines improve over name-only prompts. We then construct a hybrid approach that selects prompt strategy per circumstance. We evaluate large
The proliferation of LLMs creates an immediate need to optimize their application for complex information extraction, particularly in sensitive domains like public health data, as reflected in this 2026 publication date.
This research provides a methodology for improving the accuracy and efficiency of information extraction from unstructured text using AI, directly impacting the utility of LLMs in critical analytical tasks.
The development of 'Complexity Score' algorithms and hybrid prompt approaches enables more nuanced and effective deployment of LLMs for tasks requiring semantic inference, moving beyond simple keyword matching.
- · AI/ML researchers
- · Public health organizations
- · Healthcare data analytics
- · NLP platform providers
- · Manual data extraction services
- · Organizations relying solely on keyword-based extraction
Improved automated extraction of critical data from unstructured text, enhancing research and policy-making in areas like public health.
Accelerated development of domain-specific AI applications that can accurately interpret complex narratives.
Reduced burden on human analysts for initial data structuring, allowing them to focus on higher-level interpretation and intervention strategies.
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Read at arXiv cs.CL