Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models | Paper Citation Record

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This paper cites Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus.

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus

Reference 1

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Unresolved cited work

Reference 2

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This paper cites Security and Privacy Challenges of Large Language Models: A Survey.

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Security and Privacy Challenges of Large Language Models: A Survey

Reference 3

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Auditing Data Provenance in Text-Generation Models

Reference 4

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This paper cites Extracting training data from large language models.

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Extracting training data from large language models

Reference 5

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This paper cites Quantifying privacy risks of masked language models using membership inference attacks.

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Quantifying privacy risks of masked language models using membership inference attacks

Reference 6

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This paper cites Exploring Memorization in Fine-tuned Language Models.

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Exploring Memorization in Fine-tuned Language Models

Reference 7

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Deep learning with differential privacy

Reference 8

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This paper cites The Algorithmic F oundations of Differential Privacy.

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models The Algorithmic F oundations of Differential Privacy

Reference 9

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Training text-to-text transformers with privacy guarantees

Reference 10

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Differential privacy has disparate impact on model accuracy

Reference 11

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This paper cites Differentially private fine-tuning of language models.

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Differentially private fine-tuning of language models

Reference 12

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This paper cites Differentially Private Bias-Term Fine-tuning of Foundation Models.

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Differentially Private Bias-Term Fine-tuning of Foundation Models

Reference 13

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This paper cites Large Language Models Can Be Strong Differentially Private Learners.

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Large Language Models Can Be Strong Differentially Private Learners

Reference 14

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This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 15

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This paper cites Parameter efficient fine tuning: A comprehensive analysis across applications, April 2024.

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Parameter efficient fine tuning: A comprehensive analysis across applications, April 2024

Reference 16

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This paper cites Lora learns less and forgets less.

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Lora learns less and forgets less

Reference 17

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This paper cites Calibrating noise to sensitivity in private data analysis.

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Calibrating noise to sensitivity in private data analysis

Reference 18

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Unresolved cited work

Reference 19

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This paper cites Opacus: User-friendly differential privacy library in pytorch.

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Opacus: User-friendly differential privacy library in pytorch

Reference 20

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Parameter-efficient transfer learning for nlp

Reference 21

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models LoRA: Low-Rank Adaptation of Large Language Models

Reference 22

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning

Reference 23

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models G-Adapter: Towards Structure-Aware Parameter-Efficient Transfer Learning for Graph Transformer Networks

Reference 24

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models PEFT-SER: On the Use of Parameter Efficient Transfer Learning Approaches For Speech Emotion Recognition Using Pre-trained Speech Models

Reference 25

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning

Reference 26

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This paper cites LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning.

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 27

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Unresolved cited work

Reference 28

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning

Reference 29

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Introducing a new privacy testing library in tensorflow, 2020

Reference 30

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Membership inference attacks against machine learning models

Reference 31

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration

Reference 32

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models An empirical analysis of memorization in fine-tuned autoregressive language models

Reference 33

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Effects of differential privacy and data skewness on membership inference vulnerability

Reference 34

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models SoK: Memorisation in machine learning

Reference 35

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Sok: Membership inference is harder than previously thought

Reference 36

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Low-cost high-power membership inference by boosting relativity

Reference 37

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Reference 38

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Learning to Poison Large Language Models for Downstream Manipulation

Reference 39

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Amplifying membership exposure via data poisoning

Reference 40

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models The secret sharer: Evaluating and testing unintended memorization in neural networks

Reference 41

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Evaluating differentially private machine learning in practice

Reference 42

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Membership inference attacks against language models via neighbourhood comparison

Reference 43

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This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 44

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 45

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Peft: State-of-the-art parameter-efficient fine-tuning methods

Reference 46

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning

Reference 47

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Inan, and Andre Manoel

Reference 48

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Unresolved cited work

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Zen and the art of model adaptation: Low-utility-cost attack mitigations in collaborative machine learning

Reference 50

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Reconstructing training data from trained neural networks

Reference 51

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Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Unresolved cited work

Reference 2022

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