PVminerLLM2: Improving Structured Extraction of Patient Voice via Preference Optimization

• URL: http://arxiv.org/abs/2606.16074v1
• Date: Mon, 15 Jun 2026 00:18:47 GMT
• Title: PVminerLLM2: Improving Structured Extraction of Patient Voice via Preference Optimization
• Abstract summary: Patient-generated text contains critical information on patients' lived experiences, social context, and care engagement.<n>Prior work introduced the PV-Miner benchmark and PVMinerLLM models for structured extraction.<n>We present PVminerLLM2, an improved set of LLMs for structured patient voice extraction.
• Score: 0.19582799051022884
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Motivation: Patient-generated text contains critical information on patients' lived experiences, social context, and care engagement, but remains largely unstructured, limiting its use in patient-centered outcomes research. Prior work introduced the PV-Miner benchmark and PVMinerLLM models for structured extraction. However, supervised fine-tuning (SFT) alone struggles with rare, fine-grained, and unevenly distributed errors, particularly in token-critical structured outputs. Results: We present PVminerLLM2, an improved set of LLMs for structured patient voice extraction that applies preference optimization to address token-critical errors beyond the reach of supervised fine-tuning. Our method introduces (i) a preference objective with token-level gated stabilization term that prevents degradation of absolute token likelihood under preference optimization, and (ii) confusion-aware preference pair construction to better capture low-separation distinctions. We further incorporate token-importance weighting and inverse-frequency reweighing to address token imbalance and class skew. Across multiple model sizes, PVMinerLLM2 consistently outperforms strong baselines, achieving gains of up to 4.43% (Code), 3.50% (Sub-code), and 1.55% (Span), and outperforms baseline LLM trained with existing preference optimization methods. Availability and Implementation: The supplementary material, code, evaluation scripts, and trained models for PVminerLLM2 are publicly available at: https://github.com/Data-Mining-Lab-Yale/PVminerLLM2

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