@inproceedings{kwak-kim-2026-gap,
title = "Gap-K{\%}: Measuring Top-1 Prediction Gap for Detecting Pretraining Data",
author = "Kwak, Minseo and
Kim, Jaehyung",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1072/",
pages = "23391--23405",
ISBN = "979-8-89176-390-6",
abstract = "The opacity of massive pretraining corpora in Large Language Models (LLMs) raises significant privacy and copyright concerns, making pretraining data detection a critical challenge.Existing state-of-the-art methods typically rely on token likelihoods, yet they often overlook the gap between the target token and the model{'}s top-1 prediction, as well as local correlations between adjacent tokens.In this work, we propose Gap-K{\%}, a novel pretraining data detection method grounded in the optimization dynamics of LLM pretraining. By analyzing the next-token prediction objective, we observe that discrepancies between the model{'}s top-1 prediction and the target token induce strong gradient signals, which are explicitly penalized during training.Motivated by this, Gap-K{\%} leverages the log probability gap between the top-1 predicted token and the target token, incorporating a sliding window strategy to capture local correlations and mitigate token-level fluctuations. Extensive experiments on the WikiMIA and MIMIR benchmarks demonstrate that Gap-K{\%} achieves state-of-the-art performance, consistently outperforming prior baselines across various model sizes and input lengths."
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<abstract>The opacity of massive pretraining corpora in Large Language Models (LLMs) raises significant privacy and copyright concerns, making pretraining data detection a critical challenge.Existing state-of-the-art methods typically rely on token likelihoods, yet they often overlook the gap between the target token and the model’s top-1 prediction, as well as local correlations between adjacent tokens.In this work, we propose Gap-K%, a novel pretraining data detection method grounded in the optimization dynamics of LLM pretraining. By analyzing the next-token prediction objective, we observe that discrepancies between the model’s top-1 prediction and the target token induce strong gradient signals, which are explicitly penalized during training.Motivated by this, Gap-K% leverages the log probability gap between the top-1 predicted token and the target token, incorporating a sliding window strategy to capture local correlations and mitigate token-level fluctuations. Extensive experiments on the WikiMIA and MIMIR benchmarks demonstrate that Gap-K% achieves state-of-the-art performance, consistently outperforming prior baselines across various model sizes and input lengths.</abstract>
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%0 Conference Proceedings
%T Gap-K%: Measuring Top-1 Prediction Gap for Detecting Pretraining Data
%A Kwak, Minseo
%A Kim, Jaehyung
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F kwak-kim-2026-gap
%X The opacity of massive pretraining corpora in Large Language Models (LLMs) raises significant privacy and copyright concerns, making pretraining data detection a critical challenge.Existing state-of-the-art methods typically rely on token likelihoods, yet they often overlook the gap between the target token and the model’s top-1 prediction, as well as local correlations between adjacent tokens.In this work, we propose Gap-K%, a novel pretraining data detection method grounded in the optimization dynamics of LLM pretraining. By analyzing the next-token prediction objective, we observe that discrepancies between the model’s top-1 prediction and the target token induce strong gradient signals, which are explicitly penalized during training.Motivated by this, Gap-K% leverages the log probability gap between the top-1 predicted token and the target token, incorporating a sliding window strategy to capture local correlations and mitigate token-level fluctuations. Extensive experiments on the WikiMIA and MIMIR benchmarks demonstrate that Gap-K% achieves state-of-the-art performance, consistently outperforming prior baselines across various model sizes and input lengths.
%U https://aclanthology.org/2026.acl-long.1072/
%P 23391-23405
Markdown (Informal)
[Gap-K%: Measuring Top-1 Prediction Gap for Detecting Pretraining Data](https://aclanthology.org/2026.acl-long.1072/) (Kwak & Kim, ACL 2026)
ACL