[2605.04221] Self-Prompting Small Language Models for Privacy-Sensitive Clinical Information Extraction
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Computer Science > Computation and Language
arXiv:2605.04221 (cs)
[Submitted on 5 May 2026]
Title:Self-Prompting Small Language Models for Privacy-Sensitive Clinical Information Extraction
Authors:Yao-Shun Chuang, Tushti Mody, Uday Pratap Singh, Shirindokht Shiraz, Chun-Teh Lee, Ryan Brandon, Muhammad F Walji, Xiaoqian Jiang, Bunmi Tokede
View a PDF of the paper titled Self-Prompting Small Language Models for Privacy-Sensitive Clinical Information Extraction, by Yao-Shun Chuang and 8 other authors
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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.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.04221 [cs.CL]
(or arXiv:2605.04221v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.04221
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yao-Shun Chuang [view email] [v1] Tue, 5 May 2026 19:03:40 UTC (1,348 KB)
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