用多模型集成提升医疗编码证据完整度，找回单模型遗漏的关键信息。
Complete Evidence Extraction with Model Ensembles: A Case Study on Medical Coding
- 通过融合多个语言模型的标记级证据，提升完整证据提取效果。
- 仅3个模型集成就超过最优单模型，召回率显著提升。
- 适合医疗合规、医保结算等需完整决策依据的高风险场景。
高风险决策依赖明确证据支持。现有研究多关注短而充分的证据，但监管合规与医疗计费要求完整的证据：所有支撑决策的相关输入标记。本文将完整证据提取定义为一项任务，并在医疗编码场景中开展案例研究。受Rashomon效应启发，我们聚合多个语言模型的标记级证据以提高证据完整性。基于性能相当的现有模型、特征归因方法及人工标注证据的数据集进行实验。结果表明，Rashomon集成可显著提升证据召回率，仅带来少量额外标记开销。仅三个模型的集成即超越最优单模型，恢复了个体模型遗漏的信息。
原文摘要 · Abstract (English)
High-stakes decisions informed by decision support systems require explicit evidence. While prior work focuses on short sufficient evidence, regulatory compliance and medical billing call for complete evidence: all relevant input tokens that support a decision. We formulate complete evidence extraction as a task and study it in a medical coding setting. Motivated by the Rashomon effect, we aggregate token-level evidence from multiple language models to increase evidence completeness. We perform a case study using existing equally-performing models, feature attributions, and a dataset with human-annotated evidence. Our results show that Rashomon ensembles significantly increase evidence recall while incurring only a small token overhead over individual models. Ensembles of only three models already outperform the best single model and recover information that individual models miss.
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