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 | 173* | 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 | 155 | 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 | 48* | 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 | 18* | |
Transformers Can Do Arithmetic with the Right Embeddings S McLeish, A Bansal, A Stein, N Jain, J Kirchenbauer, BR Bartoldson, ... ICML 2024 Workshop on LLMs and Cognition, 2024 | 7* | 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, ... arXiv preprint arXiv:2406.10209, 2024 | 2* | 2024 |
LiveBench: A Challenging, Contamination-Free LLM Benchmark C White, S Dooley, M Roberts, A Pal, B Feuer, S Jain, R Shwartz-Ziv, ... arXiv preprint arXiv:2406.19314, 2024 | 1 | 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 | 1 | 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 |
How to Do a Vocab Swap? A Study of Embedding Replacement for Pre-trained Transformers N Jain, J Kirchenbauer, J Geiping, T Goldstein | | |