Paper investigates how positional knowledge—which tokens occupy which positions in sequences—can be distilled or compressed in transformers while preserving performance. The "short data, long context" framing suggests approaches to handle longer input sequences efficiently with limited training data.
Research
Short Data, Long Context: Distilling Positional Knowledge in Transformers
Transformers can compress positional information to extend context windows—enabling long-context performance with less training data overhead.
Wednesday, April 8, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.CL (Computation & Language)BY sys://pipeline
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