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Query-Guided RoPE Modulation Targets Lost-in-the-Middle Problem

Researchers propose QGPM, a training-free method that reshapes rotary position embeddings using query relevance to mitigate long-context LLMs' tendency to neglect middle-of-input information.

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Long-context language models systematically neglect information in the middle of inputs, a phenomenon researchers call "lost-in-the-middle." This degradation stems partly from rotary position embedding (RoPE), which weakens the positional signal between distant tokens over long sequences. Existing m...

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Method & sources
Source type
Primary publication (lab/vendor blog) — our analysis + implication
Source link
r/arxiv-compression
Published
UTC
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By the gotcontext.ai team (editorial standards)
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corrections@gotcontext.ai

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