LLMs outperform fine-tuned RoBERTa on low-prevalence inferentially complex circumstances in NVDRS data, with a hybrid prompt-selection framework based on a new Complexity Score generalizing across multiple frontier models.
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Comparing LLM and Fine-Tuned Model Performance on NVDRS Circumstance Extraction with Varying Prompt Complexity
LLMs outperform fine-tuned RoBERTa on low-prevalence inferentially complex circumstances in NVDRS data, with a hybrid prompt-selection framework based on a new Complexity Score generalizing across multiple frontier models.