Inhibitory Attention for Clinical Long-Context Reasoning: Characterizing and Mitigating Lost-in-the-Middle Effects in EHR Processing

Author1, Author2, Author3, Author4, Author5

EHRcontext-selectionclinical reasoninginformation retrieval

2608.20348

Builder Relevance

80%

2h ago

Abstract

This paper addresses the clinical lost-in-the-middle (CLitM) problem in electronic health records (EHR) processing, proposing a novel method to improve information retrieval accuracy.

Reality Card

Core Claim

The introduction of Query-Conditioned Clinical Suppression (QCCS) significantly improves instruction-following accuracy in EHR processing, outperforming traditional retrieval methods.

Method / Result

QCCS achieved an overall accuracy of 25.3%, compared to a maximum of 3.6% for retrieval-only comparators.

Limitations

The proof-of-concept nature of the evaluation may limit generalizability and reproducibility across different EHR datasets.

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