Min-K% Prob detects pretraining data in LLMs by flagging outlier low-probability words in text, achieving 7.4% better performance than prior methods on the new WIKIMIA benchmark.
Membership infer- ence attack susceptibility of clinical language models,
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A masked-token hit-rate comparison method detects pretraining data membership in black-box LLMs with performance comparable to white-box approaches.
Set-level data entropy estimators show linear correlation with LLM memorization scores, forming the Entropy-Memorization Linearity.
Authors introduce MLM and CLM specialization methods that avoid memorizing identifiers in sensitive training data while aiming for a privacy-utility tradeoff on medical datasets.
citing papers explorer
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MC-PDD: Masked Corpus-Level Pretraining Data Detection for Black-Box Large Language Models
A masked-token hit-rate comparison method detects pretraining data membership in black-box LLMs with performance comparable to white-box approaches.
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Data Compressibility Quantifies LLM Memorization
Set-level data entropy estimators show linear correlation with LLM memorization scores, forming the Entropy-Memorization Linearity.
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Towards the Anonymization of the Language Modeling
Authors introduce MLM and CLM specialization methods that avoid memorizing identifiers in sensitive training data while aiming for a privacy-utility tradeoff on medical datasets.