6 citations · 9 across the 10 of their papers we have counts for
4 papers · 1 filter
Watermarking for Proprietary Dataset Protection
John Kirchenbauer, Brian R. Bartoldson, Bhavya Kailkhura +1
A growing body of literature suggests that training data membership inference problems are fundamentally hard tasks in modern language modeling settings. We argue that output water…
Do Language Models Need Sleep? Offline Recurrence for Improved Online Inference
Sangyun Lee, Sean McLeish, Tom Goldstein +1
Transformer-based large language models are increasingly used for long-horizon tasks; however, their attention mechanism scales poorly with context length. To handle this, we study…
Multi-Token Prediction via Self-Distillation
John Kirchenbauer, Abhimanyu Hans, Brian Bartoldson +3
Existing techniques for accelerating language model inference, such as speculative decoding, require training auxiliary speculator models and building and deploying complex inferen…
Antidistillation Fingerprinting
Yixuan Even Xu, John Kirchenbauer, Yash Savani +5
Model distillation enables efficient emulation of frontier large language models (LLMs), creating a need for robust mechanisms to detect when a third-party student model has traine…