PARTREP: Learning What to Repeat for Decoder-only LLMs

arXiv cs.CL4w4 min read

arXiv:2607.01792v1 Announce Type: new Abstract: While decoder-only LLMs excel at a vast array of natural language tasks, it suffers from an asymmetric information flow induced by causal attention: later tokens are richer in contextual grounding than earlier ones. A simple and effective remedy is prompt repetition -- just appending a second copy of prompt before generation can redistribute grounding across positions and improve reasoning performance. However, full repetition of the original prompt doubles the KV cache footprint and quadruples attention cost during prefill, making it impractical

Read the full story at arXiv cs.CL →

PARTREP: Learning What to Repeat for Decoder-only LLMs

More in Machine Learning

Reducing Human Annotation with ML Active Learning

Learning What Matters: Supervising Sparse Attention Routing with Causal Evidence Sets

On the Depth Scalability of Logic Gate Networks

Pretraining EHR Foundation Models with Patient-Aware Sampling

From Perturbation Correction to Geometry-Aware Sampling: Sharpness-Guided Equilibrium Sampling for Balanced Flat Minima in Long-Tailed Learning

Parameter-free Adaptive Sparse Attention via Compression-Based Content Selection