Research
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.
1 min read
Sourcer/arxiv-compression
Ask AI about this articleLong-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
- Byline
- By the gotcontext.ai team (editorial standards)
- Correction?
- corrections@gotcontext.ai
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