Suivre
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
5642023
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
1932024
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
1402023
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
113*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
752023
Neftune: Noisy embeddings improve instruction finetuning
N Jain, P Chiang, Y Wen, J Kirchenbauer, HM Chu, G Somepalli, ...
arXiv preprint arXiv:2310.05914, 2023
57*2023
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
422023
Bring Your Own Data! Self-Sensitivity Evaluation for Large Language Models
N Jain, K Saifullah, Y Wen, J Kirchenbauer, M Shu, A Saha, M Goldblum, ...
First Conference on Language Modeling, 0
22*
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, ...
192021
GenQA: Generating Millions of Instructions from a Handful of Prompts
J Chen, R Qadri, Y Wen, N Jain, J Kirchenbauer, T Zhou, T Goldstein
arXiv preprint arXiv:2406.10323, 2024
62024
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
62024
LMD3: Language Model Data Density Dependence
J Kirchenbauer, G Honke, G Somepalli, J Geiping, D Ippolito, K Lee, ...
arXiv preprint arXiv:2405.06331, 2024
62024
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
42024
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
22024
What is Your Metric Telling You? Evaluating Classifier Calibration under Context-Specific Definitions of Reliability
J Kirchenbauer, J Oaks, E Heim
22022
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
Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers
S Singh, P Singhania, A Ranjan, J Kirchenbauer, J Geiping, Y Wen, ...
2024 SC24: International Conference for High Performance Computing …, 2024
2024
Extracting Parameter Counts from Fine-Tuning APIs
SM McLeish, A Hans, J Kirchenbauer, Y Wen, A Schwarzschild, J Geiping, ...
How to Do a Vocab Swap? A Study of Embedding Replacement for Pre-trained Transformers
N Jain, J Kirchenbauer, J Geiping, T Goldstein
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