Katherine Lee
Katherine Lee
Researcher, Google DeepMind
Verified email at - Homepage
Cited by
Cited by
Exploring the limits of transfer learning with a unified text-to-text transformer
C Raffel, N Shazeer, A Roberts, K Lee, S Narang, M Matena, Y Zhou, W Li, ...
The Journal of Machine Learning Research 21 (1), 5485-5551, 2020
Palm: Scaling language modeling with pathways
A Chowdhery, S Narang, J Devlin, M Bosma, G Mishra, A Roberts, ...
Journal of Machine Learning Research 24 (240), 1-113, 2023
Extracting Training Data from Large Language Models.
N Carlini, F Tramer, E Wallace, M Jagielski, A Herbert-Voss, K Lee, ...
USENIX Security Symposium 6, 2021
PaLM 2 Technical Report
R Anil, AM Dai, O Firat, M Johnson, D Lepikhin, A Passos, S Shakeri, ...
arXiv preprint arXiv:2305.10403, 2023
Gemini: a family of highly capable multimodal models
G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ...
arXiv preprint arXiv:2312.11805, 2023
Quantifying Memorization Across Neural Language Models
N Carlini, D Ippolito, M Jagielski, K Lee, F Tramer, C Zhang
arXiv preprint arXiv:2202.07646, 2022
Deduplicating training data makes language models better
K Lee, D Ippolito, A Nystrom, C Zhang, D Eck, C Callison-Burch, N Carlini
arXiv preprint arXiv:2107.06499, 2021
Gemma: Open Models Based on Gemini Research and Technology
G Team, T Mesnard, C Hardin, R Dadashi, S Bhupatiraju, S Pathak, ...
arXiv preprint arXiv:2403.08295, 2024
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ...
arXiv preprint arXiv:2403.05530, 2024
WT5?! Training Text-to-Text Models to Explain their Predictions
S Narang, C Raffel, K Lee, A Roberts, N Fiedel, K Malkan
arXiv preprint arXiv:2004.14546, 2020
Are aligned neural networks adversarially aligned?
N Carlini, M Nasr, CA Choquette-Choo, M Jagielski, I Gao, PWW Koh, ...
Advances in Neural Information Processing Systems 36, 2024
What Does it Mean for a Language Model to Preserve Privacy?
H Brown, K Lee, F Mireshghallah, R Shokri, F Tramèr
2022 ACM Conference on Fairness, Accountability, and Transparency, 2280-2292, 2022
Hallucinations in neural machine translation
K Lee, O Firat, A Agarwal, C Fannjiang, D Sussillo
Scalable Extraction of Training Data from (Production) Language Models
M Nasr, N Carlini, J Hayase, M Jagielski, AF Cooper, D Ippolito, ...
arXiv preprint arXiv:2311.17035, 2023
Counterfactual memorization in neural language models
C Zhang, D Ippolito, K Lee, M Jagielski, F Tramèr, N Carlini
Advances in Neural Information Processing Systems 36, 39321-39362, 2023
Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy
D Ippolito, F Tramèr, M Nasr, C Zhang, M Jagielski, K Lee, ...
arXiv preprint arXiv:2210.17546, 2022
Propagation of information along the cortical hierarchy as a function of attention while reading and listening to stories
M Regev, E Simony, K Lee, KM Tan, J Chen, U Hasson
Cerebral Cortex 29 (10), 4017-4034, 2019
Measuring Forgetting of Memorized Training Examples
M Jagielski, O Thakkar, F Tramèr, D Ippolito, K Lee, N Carlini, E Wallace, ...
arXiv preprint arXiv:2207.00099, 2022
A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity
S Longpre, G Yauney, E Reif, K Lee, A Roberts, B Zoph, D Zhou, J Wei, ...
arXiv preprint arXiv:2305.13169, 2023
Madlad-400: A multilingual and document-level large audited dataset
S Kudugunta, I Caswell, B Zhang, X Garcia, D Xin, A Kusupati, R Stella, ...
Advances in Neural Information Processing Systems 36, 2024
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