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Petros Karypis

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#natural language process... Preprint Sep 2026

How Calibration Content Shapes Attention-Based Reranking

Attention-based rerankers score documents by aggregating query-to-document attention and subtracting a null-query calibration pass to remove positional and structural bias. Although widely used, this calibration assumes that the null pass removes irrelevant signal from each document. We show that modern prompt content,...

Petros Karypis, Hossein Rajaby Faghihi, Peter Chen et al. · 0 citations
Jul 2026

LeRoPE: Learnable RoPE Frequencies Improve Language Modeling

This work modify RoPE by learning a scalar per frequency, treating frequencies as learnable parameters rather than hyperparameters, and observes and analyzes the emergence of a high-norm, positional LeRoPE band.

Petros Karypis, Sean O'Brien, Shreyas Kadekodi et al. · 0 citations

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