发表机构
Healthee（Healthee）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文系统性对比列表式交叉编码器微调与智能体指令微调两种LLM重排序范式，在自建医疗流程重排序数据集上，发现1.09亿参数的ListNet微调交叉编码器性能优于40亿参数的Qwen3-Reranker-4B，参数量仅为后者1/37，相关成果已开源。
AI 中文摘要
针对患者查询的医疗流程重排序是健康保险信息检索的关键组成部分，但受限于患者语言与临床术语间存在的巨大词汇鸿沟。本文针对该生产任务的两种重排序范式开展系统性对比研究：（1）采用列表式学习排序目标并在不同层冻结配置下微调的小型交叉编码器（MedCPT、MiniLM-L12）；（2）Qwen3-Reranker-4B，一款40亿参数的指令重排序器，其提示词通过GPT-4.1驱动的智能体优化循环迭代优化。在自建的包含708项保险服务、2647条查询的数据集上，研究发现：参数规模仅为1.09亿的交叉编码器（采用ListNet微调），在NDCG@3指标上比40亿参数的Qwen3-Reranker-4B高出2.6个百分点，在斯皮尔曼相关系数上高出13.3个点，且参数量仅为后者的1/37。研究还报告了实践发现、可扩展的基于大语言模型的数据集构建流程，以及与生产级重排序系统相关的部署权衡，并发布了代码和示例数据集以支持可复现性及向其他领域的迁移适配。
英文摘要
Reranking medical procedures against patient queries is a critical component of health insurance information retrieval, complicated by a substantial lexical gap between patient language and clinical nomenclature. We present a systematic comparison of two reranking paradigms for this production task: (1) small cross-encoders (MedCPT, MiniLM-L12) fine-tuned with listwise learning-to-rank objectives across layer freezing configurations, and (2) Qwen3-Reranker-4B, a 4B-parameter instruction reranker whose prompt is iteratively refined via an agentic optimization loop driven by GPT-4.1. On a purpose-built dataset of 2,647 queries across 708 insurance services, we find that a 109M-parameter cross-encoder fine-tuned with ListNet outperforms the 4B-parameter model by 2.6 percentage points on NDCG@3 and 13.3 points on Spearman correlation - at 37x fewer parameters. We report practical findings, a scalable LLM based dataset construction pipeline, and deployment trade-offs relevant to production reranking systems. We release our code and a sample dataset to support reproducibility and adaptation to other domains.
Comments10 pages, 6 figures, 4 tables. Code available at https://github.com/matanf-healthee/listwise-crossencoder-reranking
