| 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 | 636 | 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 | 304* | 2023 |
| 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 | 228 | 2024 |
| 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 | 157 | 2023 |
| 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 | 132* | 2023 |
| Neftune: Noisy embeddings improve instruction finetuning N Jain, P Chiang, Y Wen, J Kirchenbauer, HM Chu, G Somepalli, ... International Conference on Learning Representations (ICLR) 2024, 2024 | 81* | 2024 |
| 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 | 51 | 2023 |
| 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 | 24* | |
| 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, ... | 22 | 2021 |
| 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 | 8 | 2024 |
| 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 | 7 | 2024 |
| 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 | 7 | 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 | 6 | 2024 |
| Avi Schwarzschild, et al. 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 | 4 | 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 | 3 | 2024 |
| What is Your Metric Telling You? Evaluating Classifier Calibration under Context-Specific Definitions of Reliability J Kirchenbauer, J Oaks, E Heim | 2 | 2022 |
| 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 | 1 | 2022 |
| How to Do a Vocab Swap? A Study of Embedding Replacement for Pre-trained Transformers N Jain, J Kirchenbauer, J Geiping, T Goldstein | 1 | |
| Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers S Singh, P Singhania, A Ranjan, J Kirchenbauer, J Geiping, Y Wen, ... SC24: International Conference for High Performance Computing, Networking …, 2024 | | 2024 |
| GenQA: An Instruction Dataset of LLM Generated Questions and Answers J Chen, R Qadri, Y Wen, N Jain, J Kirchenbauer, T Zhou, T Goldstein | | |
Tom GoldsteinVolpi-Cupal Professor of Computer Science, University of MarylandVerified email at cs.umd.edu