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Archit Uniyal

2 accepted papers

2022

An Empirical Analysis of Memorization in Fine-tuned Autoregressive Language Models

EMNLP 2022main

Large language models are shown to present privacy risks through memorization of training data, andseveral recent works have studied such risks for the pre-training phase. Little attention, however, has been given to the fine-tuning phase and it is not well understood how different fine-tuning metho…

Cited by 88SourcePDFScholar
2022

Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks

EMNLP 2022main

The wide adoption and application of Masked language models (MLMs) on sensitive data (from legal to medical) necessitates a thorough quantitative investigation into their privacy vulnerabilities. Prior attempts at measuring leakage of MLMs via membership inference attacks have been inconclusive, imp…

Cited by 177SourcePDFScholar