Papers with Positional Decay Reweighting

1 papers
PDR: A Plug-and-Play Positional Decay Framework for LLM Pre-training Data Detection (2026.acl-long)

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Challenge: Existing likelihood-based methods for detecting pretraining data are limited in black-box, zero-shot settings.
Approach: They propose a training-free and plug-and-play framework that reweights token-level scores to amplify distinct signals from early positions while suppressing noise from later ones.
Outcome: The proposed framework amplifys signals from early positions while suppressing noise from later positions.

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