Self-Prompting Small Language Models for Privacy-Sensitive Clinical Information Extraction

• URL: http://arxiv.org/abs/2605.04221v1
• Date: Tue, 05 May 2026 19:03:40 GMT
• Title: Self-Prompting Small Language Models for Privacy-Sensitive Clinical Information Extraction
• Abstract summary: We developed a framework that enables small language models to self-generate, verify, refine, and evaluate entity-specific prompts.<n>We evaluated candidate open-weight models with multi-prompt ensemble inference and selected models using QLoRA-based supervised fine-tuning and direct preference optimization.<n>These findings suggest that automated prompt optimization combined with lightweight preference-based post-training can support scalable clinical information extraction.
• Score: 5.6073733544594395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clinical named entity recognition from dental progress notes is challenging because documentation is highly unstructured, domain-specific, and often privacy-sensitive. We developed a locally deployable framework that enables small language models to self-generate, verify, refine, and evaluate entity-specific prompts for extracting multiple clinical entities from dental notes. Using 1,200 annotated notes, we evaluated candidate open-weight models with multi-prompt ensemble inference and further adapted selected models using QLoRA-based supervised fine-tuning and direct preference optimization. Model performance varied substantially, highlighting the need for task-specific evaluation rather than reliance on generic benchmarks. Qwen2.5-14B-Instruct achieved the strongest baseline performance. After DPO, Qwen2.5-14B-Instruct and Llama-3.1-8B-Instruct achieved micro/macro F1 scores of 0.864/0.837 and 0.806/0.797, respectively. These findings suggest that automated prompt optimization combined with lightweight preference-based post-training can support scalable clinical information extraction using locally deployed small language models.

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