| 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 | 346* | 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 Conference on Neural Information Processing Systems (NeurIPS) 2023, 2023 | 248 | 2023 |
| NEFTune: Noisy embeddings improve instruction finetuning N Jain, P yeh Chiang, Y Wen, J Kirchenbauer, HM Chu, G Somepalli, ... The Twelfth International Conference on Learning Representations, 2023 | 89* | 2023 |
| LiveBench: A Challenging, Contamination-Free LLM Benchmark C White, S Dooley, M Roberts, A Pal, B Feuer, S Jain, R Shwartz-Ziv, ... International Conference on Learning Representations (ICLR) 2025, 2024 | 65* | 2024 |
| Transformers Can Do Arithmetic with the Right Embeddings S McLeish, A Bansal, A Stein, N Jain, J Kirchenbauer, BR Bartoldson, ... Conference on Neural Information Processing Systems (NeurIPS) 2024, 2024 | 36* | 2024 |
| 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, 2024 | 23* | 2024 |
| 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, ... Conference on Neural Information Processing Systems (NeurIPS) 2024, 2024 | 19* | 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 | 9 | 2024 |
| Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach J Geiping, S McLeish, N Jain, J Kirchenbauer, S Singh, BR Bartoldson, ... arXiv preprint arXiv:2502.05171, 2025 | 3 | 2025 |
| How to do a vocab swap? a study of embedding replacement for pre-trained transformers N Jain, J Kirchenbauer, J Geiping, T Goldstein | 2 | 2022 |
| 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 | 1 | 2024 |
| Exploiting Sparsity for Long Context Inference: Million Token Contexts on Commodity GPUs R Synk, M Hoover, J Kirchenbauer, N Jain, A Stein, M Shu, JM Sanchez, ... arXiv preprint arXiv:2502.06766, 2025 | | 2025 |
| Refusal Tokens: A Simple Way to Calibrate Refusals in Large Language Models N Jain, A Shrivastava, C Zhu, D Liu, A Samuel, A Panda, A Kumar, ... Neurips Safe Generative AI Workshop 2024, 2024 | | 2024 |
| Multi-color forcing in graphs C Bozeman, PE Harris, N Jain, B Young, T Yu Graphs and Combinatorics 36 (6), 1855-1868, 2020 | | 2020 |
John KirchenbauerUniversity of Maryland, College ParkZweryfikowany adres z umd.edu