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John Kirchenbauer
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A watermark for large language models
J Kirchenbauer, J Geiping, Y Wen, J Katz, I Miers, T Goldstein
International Conference on Machine Learning, 17061-17084, 2023
3532023
Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery
Y Wen, N Jain, J Kirchenbauer, M Goldblum, J Geiping, T Goldstein
Advances in Neural Information Processing Systems 36, 2024
1232024
On the reliability of watermarks for large language models
J Kirchenbauer, J Geiping, Y Wen, M Shu, K Saifullah, K Kong, ...
arXiv preprint arXiv:2306.04634, 2023
772023
Tree-ring watermarks: Fingerprints for diffusion images that are invisible and robust
Y Wen, J Kirchenbauer, J Geiping, T Goldstein
arXiv preprint arXiv:2305.20030, 2023
57*2023
Baseline defenses for adversarial attacks against aligned language models
N Jain, A Schwarzschild, Y Wen, G Somepalli, J Kirchenbauer, P Chiang, ...
arXiv preprint arXiv:2309.00614, 2023
322023
GOAT: A global transformer on large-scale graphs
K Kong, J Chen, J Kirchenbauer, R Ni, CB Bruss, T Goldstein
International Conference on Machine Learning, 17375-17390, 2023
252023
Neftune: Noisy embeddings improve instruction finetuning
N Jain, P Chiang, Y Wen, J Kirchenbauer, HM Chu, G Somepalli, ...
arXiv preprint arXiv:2310.05914, 2023
22*2023
Bring your own data! self-supervised evaluation for large language models
N Jain, K Saifullah, Y Wen, J Kirchenbauer, M Shu, A Saha, M Goldblum, ...
arXiv preprint arXiv:2306.13651, 2023
172023
A closer look at distribution shifts and out-of-distribution generalization on graphs
M Ding, K Kong, J Chen, J Kirchenbauer, M Goldblum, D Wipf, F Huang, ...
162021
Avi Schwarzschild, Aniruddha Saha, et al. Neftune: Noisy embeddings improve instruction finetuning
N Jain, P Chiang, Y Wen, J Kirchenbauer, HM Chu, G Somepalli, ...
arXiv preprint arXiv:2310.05914 2 (3), 2023
72023
A watermark for large language models (2023)
J Kirchenbauer, J Geiping, Y Wen, J Katz, I Miers, T Goldstein
arXiv preprint arXiv:2301.10226, 2023
72023
yeh Chiang
N Jain, A Schwarzschild, Y Wen, G Somepalli, J Kirchenbauer
P., Goldblum, M., Saha, A., Geiping, J., and Goldstein, T. Baseline defenses …, 2023
42023
Transformers Can Do Arithmetic with the Right Embeddings
S McLeish, A Bansal, A Stein, N Jain, J Kirchenbauer, BR Bartoldson, ...
arXiv preprint arXiv:2405.17399, 2024
12024
Knowing When You Don’t Know: Quantifying and Reasoning about Uncertainty in Machine Learning Models
E Heim, J Kirchenbauer, J Helland, J Oaks, A Singh, Z Lipton
12022
What is Your Metric Telling You? Evaluating Classifier Calibration under Context-Specific Definitions of Reliability
J Kirchenbauer, J Oaks, E Heim
12022
Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs
A Hans, Y Wen, N Jain, J Kirchenbauer, H Kazemi, P Singhania, S Singh, ...
arXiv preprint arXiv:2406.10209, 2024
2024
OPTune: Efficient Online Preference Tuning
L Chen, J Chen, C Liu, J Kirchenbauer, D Soselia, C Zhu, T Goldstein, ...
arXiv preprint arXiv:2406.07657, 2024
2024
LMD3: Language Model Data Density Dependence
J Kirchenbauer, G Honke, G Somepalli, J Geiping, D Ippolito, K Lee, ...
arXiv preprint arXiv:2405.06331, 2024
2024
How to Do a Vocab Swap? A Study of Embedding Replacement for Pre-trained Transformers
N Jain, J Kirchenbauer, J Geiping, T Goldstein
2022
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Artiklar 1–19