用内部键值记忆取代外部检索，提升医疗大模型响应速度与准确性
Efficient and Effective Internal Memory Retrieval for LLM-Based Healthcare Prediction
- 将关键医疗信息编码进模型参数，实现无延迟内部快速检索
- 在四个医疗预测数据集上达到当前最佳性能，准确率显著提升
- 适合对实时性要求高的临床决策支持系统使用
大型语言模型在医疗领域潜力巨大，但其在高风险临床场景中的可靠性常因幻觉和缺乏精细医学上下文而受损。尽管检索增强生成（RAG）可缓解这些问题，但标准监督管道需对大规模外部知识库进行计算密集型搜索，导致高延迟，不适用于时间敏感的诊疗。为此，我们提出K2K框架，以内部键值记忆替代外部检索。通过将关键临床信息直接编码至模型参数空间，K2K实现了无需推理时开销的内部快速检索。我们进一步通过激活引导的探针构建和交叉注意力重排序来提升检索质量。实验结果表明，K2K在四个基准医疗结局预测数据集上均达到最先进性能。
原文摘要 · Abstract (English)
Large language models (LLMs) hold significant promise for healthcare, yet their reliability in high-stakes clinical settings is often compromised by hallucinations and a lack of granular medical context. While Retrieval Augmented Generation (RAG) can mitigate these issues, standard supervised pipelines require computationally intensive searches over massive external knowledge bases, leading to high latency that is impractical for time-sensitive care. To address this, we introduce Keys to Knowledge (K2K), a novel framework that replaces external retrieval with internal, key-based knowledge access. By encoding essential clinical information directly into the model's parameter space, K2K enables rapid retrieval from internal key-value memory without inference-time overhead. We further enhance retrieval quality through activation-guided probe construction and cross-attention reranking. Experimental results demonstrate that K2K achieves state-of-the-art performance across four benchmark healthcare outcome prediction datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。