5 accepted papers
Large language models (LLMs) have demonstrated impressive performance across a wide range of tasks, including open-ended dialogue, driving advancements in virtual assistants and other interactive systems. However, these models often generate outputs misaligned with human values, such as ethical norm…
Federated learning (FL) enables privacy-preserving distributed machine learning by sharing gradients instead of raw data. However, FL remains vulnerable to gradient inversion attacks, in which shared gradients can reveal sensitive training data. Prior research has mainly concentrated on unimodal tas…
Distinguishing between real and AI-generated images, commonly referred to as 'image detection', presents a timely and significant challenge. Despite extensive research in the (semi-)supervised regime, zero-shot and few-shot solutions have only recently emerged as promising alternatives. Their main…
Auditing autoregressive Large Language Models (LLMs) for disparities is often impeded by high token costs and limited precision. We introduce Token-Focused Disparity Probing (TFDP), a novel methodology overcoming these challenges by adapting single-token masked prediction to autoregressive architect
"Diffusion models, which revolutionized image generation, are facing challenges related to intellectual property. These challenges arise when a generated image is influenced by copyrighted images from the training data, a plausible scenario in internet-collected data. Hence, pinpointing influential…