Inhibitory attention for clinical long-context reasoning: characterizing and mitigating lost-in-the-middle effects in EHR processing
Read the original at arxiv.org→arXiv:2608.20348v1 Announce Type: new Abstract: Electronic health records now routinely exceed 100,000 tokens per patient. Yet large language models exhibit the lost-in-the-middle (LitM) effect: information near the...
Original headline: "Inhibitory Attention for Clinical Long-Context Reasoning: Characterizing and Mitigating Lost-in-the-Middle Effects in EHR Processing"
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- Aug 24, 04:00 UTC arXiv cs.CL lead source Inhibitory Attention for Clinical Long-Context Reasoning: Characterizing and Mitigating Lost-in-the-Middle Effects in EHR Processing