John Kirchenbauer

According to our database, John Kirchenbauer authored at least 16 papers between 2022 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

GenQA: Generating Millions of Instructions from a Handful of Prompts.

Jiuhai Chen, Rifaa Qadri, Yuxin Wen, Neel Jain, John Kirchenbauer, Tianyi Zhou, Tom Goldstein

CoRR, 2024

Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs.

Abhimanyu Hans, Yuxin Wen, Neel Jain, John Kirchenbauer, Hamid Kazemi, Prajwal Singhania, Siddharth Singh, Gowthami Somepalli, Jonas Geiping, Abhinav Bhatele, Tom Goldstein

CoRR, 2024

OPTune: Efficient Online Preference Tuning.

Lichang Chen, Jiuhai Chen, Chenxi Liu, John Kirchenbauer, Davit Soselia, Chen Zhu, Tom Goldstein, Tianyi Zhou, Heng Huang

CoRR, 2024

Transformers Can Do Arithmetic with the Right Embeddings.

Sean McLeish, Arpit Bansal, Alex Stein, Neel Jain, John Kirchenbauer, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Jonas Geiping, Avi Schwarzschild, Tom Goldstein

CoRR, 2024

LMD3: Language Model Data Density Dependence.

John Kirchenbauer, Garrett Honke, Gowthami Somepalli, Jonas Geiping, Daphne Ippolito, Katherine Lee, Tom Goldstein, David Andre

CoRR, 2024

Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers.

Siddharth Singh, Prajwal Singhania, Aditya Ranjan, John Kirchenbauer, Jonas Geiping, Yuxin Wen, Neel Jain, Abhimanyu Hans, Manli Shu, Aditya Tomar, Tom Goldstein, Abhinav Bhatele

Proceedings of the International Conference for High Performance Computing, 2024

On the Reliability of Watermarks for Large Language Models.

John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, Tom Goldstein

Proceedings of the Twelfth International Conference on Learning Representations, 2024

NEFTune: Noisy Embeddings Improve Instruction Finetuning.

Neel Jain, Ping-yeh Chiang, Yuxin Wen, John Kirchenbauer, Hong-Min Chu, Gowthami Somepalli, Brian R. Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Baseline Defenses for Adversarial Attacks Against Aligned Language Models.

Neel Jain, Avi Schwarzschild, Yuxin Wen, Gowthami Somepalli, John Kirchenbauer, Ping-yeh Chiang, Micah Goldblum, Aniruddha Saha, Jonas Geiping, Tom Goldstein

CoRR, 2023

Bring Your Own Data! Self-Supervised Evaluation for Large Language Models.

Neel Jain, Khalid Saifullah, Yuxin Wen, John Kirchenbauer, Manli Shu, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein

CoRR, 2023

Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust.

Yuxin Wen, John Kirchenbauer, Jonas Geiping, Tom Goldstein

CoRR, 2023

Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images.

Yuxin Wen, John Kirchenbauer, Jonas Geiping, Tom Goldstein

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery.

Yuxin Wen, Neel Jain, John Kirchenbauer, Micah Goldblum, Jonas Geiping, Tom Goldstein

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

GOAT: A Global Transformer on Large-scale Graphs.

Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, Tom Goldstein

Proceedings of the International Conference on Machine Learning, 2023

A Watermark for Large Language Models.

John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein

Proceedings of the International Conference on Machine Learning, 2023

2022

What is Your Metric Telling You? Evaluating Classifier Calibration under Context-Specific Definitions of Reliability.

John Kirchenbauer, Jacob Oaks, Eric Heim

CoRR, 2022