What can we learn from Data Leakage and Unlearning for Law?

The eu proposal for a general data protection regulation and the roots of the ‘right to be forgotten’

Mantelero, A · 2013

Earlier work this paper cites.

GloVe: Global vectors for word representation

Pennington, J., Socher, R., and Manning, C · 2014

Earlier work this paper cites.

Towards making systems forget with machine unlearning

Cao, Y. and Yang, J · 2015

Earlier work this paper cites.

https://eur-lex.europa.eu/eli/reg/2016/679/2016-05-04

Lex access to european union law · 2016

Earlier work this paper cites.

Pointer sentinel mixture models, 2016

Merity, S., Xiong, C., Bradbury, J., and Socher, R · 2016

Earlier work this paper cites.

https://www.priv.gc.ca/en/opc-news/news-and-announcements/2018/an_181010/ , October 2018

Privacy commissioner seeks federal court determination on key issue for canadians’ online reputation · 2018

Earlier work this paper cites.

Hierarchical neural story generation

Fan, A., Lewis, M., and Dauphin, Y · 2018

Earlier work this paper cites.

The secret sharer: Evaluating and testing unintended memorization in neural networks

Carlini, N., Liu, C., Erlingsson, Ú., Kos, J., and Song, D · 2019

Earlier work this paper cites.

BERT: Pre-training of deep bidirectional transformers for language understanding

Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019

Cited alongside, same era.

Machine unlearning, 2020

Bourtoule, L., Chandrasekaran, V., Choquette-Choo, C. A., Jia, H., Travers, A., Zhang, B., Lie, D., and Papernot, N · 2020

Cited alongside, same era.

Extracting training data from large language models

Carlini, N., Tramèr, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, Ú., Oprea, A., and Raffel, C · 2021

Cited alongside, same era.

Does BERT pretrained on clinical notes reveal sensitive data?

Lehman, E., Jain, S., Pichotta, K., Goldberg, Y., and Wallace, B · 2021

Cited alongside, same era.

Neuro-symbolic language modeling with automaton-augmented retrieval

Alon, U., Xu, F., He, J., Sengupta, S., Roth, D., and Neubig, G · 2022

Cited alongside, same era.

The privacy onion effect: Memorization is relative

Deduplicating training data mitigates privacy risks in language models, 2022

Kandpal, N., Wallace, E., and Raffel, C · 2022

Later among the works it cites.

An empirical analysis of memorization in fine-tuned autoregressive language models

Mireshghallah, F., Uniyal, A., Wang, T., Evans, D., and Berg-Kirkpatrick, T · 2022

Later among the works it cites.

Deduplicating training data makes language models better, 2022

Nystrom, A., Zhang, C., Callison-Burch, C., Ippolito, D., Eck, D., Lee, K., and Carlini, N · 2022

Later among the works it cites.

Emergent and predictable memorization in large language models, 2023

Biderman, S., Prashanth, U. S., Sutawika, L., Schoelkopf, H., Anthony, Q., Purohit, S., and Raf, E · 2023

Closest in time.

Quantifying memorization across neural language models, 2023

Carlini, N., Ippolito, D., Jagielski, M., Lee, K., Tramer, F., and Zhang, C · 2023

Closest in time.

Analyzing leakage of personally identifiable information in language models, 2023