10 papers
SEAL: Semantic Aware Image Watermarking
Kasra Arabi, R. Teal Witter, Chinmay Hegde +1
Generative models have rapidly evolved to generate realistic outputs. However, their synthetic outputs increasingly challenge the clear distinction between natural and AI-generated…
On the Robustness of Watermarking for Autoregressive Image Generation
Andreas Müller, Denis Lukovnikov, Shingo Kodama +5
The proliferation of autoregressive (AR) image generators demands reliable detection and attribution of their outputs to mitigate misinformation, and to filter synthetic images fro…
Understanding Empirical Unlearning with Combinatorial Interpretability
Shingo Kodama, Niv Cohen, Micah Adler +1
While many recent methods aim to unlearn or remove knowledge from pretrained models, seemingly erased knowledge often persists and can be recovered in various ways. Because large f…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1144
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
When Are Concepts Erased From Diffusion Models?
Kevin Lu, Nicky Kriplani, Rohit Gandikota +4
In concept erasure, a model is modified to selectively prevent it from generating a target concept. Despite the rapid development of new methods, it remains unclear how thoroughly…
SimKey: A Semantically Aware Key Module for Watermarking Language Models
Shingo Kodama, Haya Diwan, Lucas Rosenblatt +2
The rapid spread of text generated by large language models (LLMs) makes it increasingly difficult to distinguish authentic human writing from machine output. Watermarking offers a…