Effective medical text retrieval requires both high accuracy and low latency. While LLM-based embedding models possess powerful retrieval capabilities, their prohibitive latency and high computational cost limit their application in real-time scenarios. Furthermore, the lack of comprehensive and high-fidelity benchmarks hinders progress in Chinese medical text retrieval. In this work, we introduce the Chinese Medical Text Embedding Benchmark (CMedTEB), a benchmark spanning three kinds of practical embedding tasks: retrieval, reranking, and semantic textual similarity (STS). Distinct from purely automated datasets, CMedTEB is curated via a rigorous multi-LLM voting pipeline validated by clinical experts, ensuring gold-standard label quality while effectively mitigating annotation noise. On this foundation, we propose the Chinese Medical Asymmetric REtriever (CARE), an asymmetric architecture that pairs a lightweight BERT-style encoder for online query encoding with a powerful LLM-based encoder for offline document encoding. However, optimizing such an asymmetric retriever with two structurally different encoders presents distinctive challenges. To address this, we introduce a novel two-stage training strategy that progressively bridges the query and document representations. Extensive experiments demonstrate that CARE surpasses state-of-the-art symmetric models on CMedTEB, achieving superior retrieval performance without increasing inference latency.

翻译：高效医学文本检索需要同时具备高准确率和低延迟。基于大语言模型的嵌入模型虽拥有强大的检索能力，但过高的延迟和计算成本限制了其在实时场景中的应用。此外，缺乏全面且高保真的基准数据集阻碍了中文医学文本检索领域的发展。本文提出了中文医学文本嵌入基准(CMedTEB)，涵盖检索、重排序和语义文本相似度三类实用嵌入任务。与纯自动化数据集不同，CMedTEB通过严格的、经临床专家验证的多大语言模型投票流水线进行管理，在有效缓解标注噪声的同时确保了黄金标准标签质量。在此基础上，我们提出了中文医学非对称检索器(CARE)，该非对称架构将轻量级BERT式编码器用于在线查询编码，与强大的基于大语言模型的编码器配对用于离线文档编码。然而，优化这种包含两个结构迥异编码器的非对称检索器存在独特挑战。为解决此问题，我们引入了一种新颖的两阶段训练策略，逐步弥合查询与文档表示之间的鸿沟。大量实验表明，CARE在CMedTEB上超越了最先进的对称模型，在不增加推理延迟的情况下实现了更优的检索性能。