115 citations · 206 across the 10 of their papers we have counts for
5 papers · 1 filter
Bring Your Own Data! Self-Supervised Evaluation for Large Language Models
Neel Jain, Khalid Saifullah, Yuxin Wen +6
With the rise of Large Language Models (LLMs) and their ubiquitous deployment in diverse domains, measuring language model behavior on realistic data is imperative. For example, a…
On the Reliability of Watermarks for Large Language Models
John Kirchenbauer, Jonas Geiping, Yuxin Wen +7
As LLMs become commonplace, machine-generated text has the potential to flood the internet with spam, social media bots, and valueless content. Watermarking is a simple and effecti…
Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust
Yuxin Wen, John Kirchenbauer, Jonas Geiping +1
Watermarking the outputs of generative models is a crucial technique for tracing copyright and preventing potential harm from AI-generated content. In this paper, we introduce a no…
Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery
Yuxin Wen, Neel Jain, John Kirchenbauer +3
The strength of modern generative models lies in their ability to be controlled through text-based prompts. Typical "hard" prompts are made from interpretable words and tokens, and…
A Watermark for Large Language Models
John Kirchenbauer, Jonas Geiping, Yuxin Wen +3
Potential harms of large language models can be mitigated by watermarking model output, i.e., embedding signals into generated text that are invisible to humans but algorithmically…