Inhibitory Attention for Clinical Long-Context Reasoning: Characterizing and Mitigating Lost-in-the-Middle Effects in EHR Processing
Researchers have identified a problem with large language models processing electronic health records (EHRs), where information in the middle of long contexts is retrieved less reliably. This 'lost-in-the-middle' effect can be critical in clinical settings, where important information may be buried in the center of a note. To address this issue, the authors propose Query-Conditioned Clinical Suppression (QCCS), a lightweight query-conditioned selection gate that outperforms o
Researchers have identified a problem with large language models processing electronic health records (EHRs), where information in the middle of long contexts is retrieved less reliably. This 'lost-in-the-middle' effect can be critical in clinical settings, where important information may be buried in the center of a note. To address this issue, the authors propose Query-Conditioned Clinical Suppression (QCCS), a lightweight query-conditioned selection gate that outperforms other context-selection strategies in retrieving relevant information from EHRs.
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Why it matters: This research matters to engineers and researchers working on AI applications in healthcare because it highlights the limitations of current language models in processing long clinical texts. The proposed QCCS method could improve the accuracy of EHR-based decision-making systems, which rely heavily on reliable retrieval of relevant information from these texts.
Source: https://arxiv.org/abs/2608.20348
This article was originally published at: https://arxiv.org/abs/2608.20348