Efficient and Effective Internal Memory Retrieval for LLM-Based Healthcare Prediction

作者：Association for Computational Linguistics 2026, Jiatan Huang, Mingchen Li, Zonghai Yao, Hong Yu · 发表于：Underline Science Inc. · 年份：2026 · DOI：10.48448/eyze-5411 · 研究领域：Computer science、Artificial intelligence、Data mining、Information retrieval、Machine learning

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.