用偏好优化提升患者文本结构化提取准确率，解决细粒度错误难题。
PVminerLLM2: Improving Structured Extraction of Patient Voice via Preference Optimization
- 引入分词级门控稳定项的偏好目标，防止优化中误标风险上升
- 在多个模型规模上实现最高4.43%的提取准确率提升
- 适合医疗文本分析、患者体验研究等需要精准结构化数据的场景
患者生成的文本包含其生活经历、社会背景和护理参与的关键信息，但多为非结构化形式，限制了在以患者为中心的结果研究中的应用。先前工作提出了PV-Miner基准和PVMinerLLM模型用于结构化提取。然而，仅靠监督微调（SFT）难以处理罕见、细粒度且分布不均的错误，尤其在对分词敏感的结构化输出中。本文提出PVminerLLM2，通过偏好优化改进大语言模型在结构化患者语音提取中的表现。方法包括：(i) 引入带有分词级门控稳定项的偏好目标，防止偏好优化过程中绝对分词概率下降；(ii) 设计混淆感知的偏好样本构建方式，更好捕捉低区分度差异。同时引入分词重要性加权与逆频率重加权，缓解分词不平衡与类别偏移问题。在多种模型规模下，PVminerLLM2持续优于强基线模型，在代码、子代码和跨度任务上分别取得最高4.43%、3.50%和1.55%的性能提升，并超越现有偏好优化训练的基线模型。相关补充材料、代码、评估脚本及训练好的模型已公开于https://github.com/Data-Mining-Lab-Yale/PVminerLLM2。
原文摘要 · Abstract (English)
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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