1 citations · 2 across the 9 of their papers we have counts for
9 papers
Reference-Based Bias Detection in LLMs via Relative Representations of Hidden States
Marek Jeliński, Jan Dubiński, Maciej Chrabaszcz +1
Existing bias auditing methods typically rely on model outputs, requiring costly benchmarks or judge models and potentially missing internal shifts that never appear in generated t…
The Reliability Gap in Benchmark Auditing: Distribution Shift and Scale as Failure Modes of Contamination Detection
Wojciech Zarzecki, Jan Dubiński, Sebastian Cygert
Benchmark contamination, where evaluation examples appear in a model's training data, threatens the validity of LLM assessment. Statistical tools for detecting training-data member…
Membership and Dataset Inference Attacks on Large Audio Generative Models
Jakub Proboszcz, Paweł Kochanski, Karol Korszun +5
Generative audio models, based on diffusion and autoregressive architectures, have advanced rapidly in both quality and expressiveness. This progress, however, raises pressing copy…
ExpertSim: Fast Particle Detector Simulation Using Mixture-of-Generative-Experts
Patryk Będkowski, Jan Dubiński, Filip Szatkowski +3
Simulating detector responses is a crucial part of understanding the inner workings of particle collisions in the Large Hadron Collider at CERN. Such simulations are currently perf…
Radioactive Watermarks in Diffusion and Autoregressive Image Generative Models
Michel Meintz, Jan Dubiński, Franziska Boenisch +1
Image generative models have become increasingly popular, but training them requires large datasets that are costly to collect and curate. To circumvent these costs, some parties m…
Efficient LLM Moderation with Multi-Layer Latent Prototypes
Maciej Chrabąszcz, Filip Szatkowski, Bartosz Wójcik +3
Although modern LLMs are aligned with human values during post-training, robust moderation remains essential to prevent harmful outputs at deployment time. Existing approaches suff…
CDI: Copyrighted Data Identification in Diffusion Models
Jan Dubiński, Antoni Kowalczuk, Franziska Boenisch +1
Diffusion Models (DMs) benefit from large and diverse datasets for their training. Since this data is often scraped from the Internet without permission from the data owners, this…
Deep Generative Models for Proton Zero Degree Calorimeter Simulations in ALICE, CERN
Patryk Będkowski, Jan Dubiński, Kamil Deja +1
Simulating detector responses is a crucial part of understanding the inner-workings of particle collisions in the Large Hadron Collider at CERN. The current reliance on statistical…
Towards More Realistic Membership Inference Attacks on Large Diffusion Models
Jan Dubiński, Antoni Kowalczuk, Stanisław Pawlak +3
Generative diffusion models, including Stable Diffusion and Midjourney, can generate visually appealing, diverse, and high-resolution images for various applications. These models…