2 citations · 3 across the 4 of their papers we have counts for
8 papers
Focused Chain-of-Thought: Efficient LLM Reasoning via Structured Input Information
Lukas Struppek, Dominik Hintersdorf, Hannah Struppek +2
Recent large language models achieve strong reasoning performance by generating detailed chain-of-thought traces, but this often leads to excessive token use and high inference lat…
Finding DoRI: Discovery of Retained Images in Diffusion Models
Antoni Kowalczuk, Dominik Hintersdorf, Lukas Struppek +3
Text-to-image diffusion models (DMs) have achieved remarkable success in image generation. However, concerns about data privacy and intellectual property remain due to their potent…
Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models
Dominik Hintersdorf, Lukas Struppek, Kristian Kersting +2
Diffusion models (DMs) produce very detailed and high-quality images. Their power results from extensive training on large amounts of data, usually scraped from the internet withou…
Exploring the Adversarial Capabilities of Large Language Models
Lukas Struppek, Minh Hieu Le, Dominik Hintersdorf +1
The proliferation of large language models (LLMs) has sparked widespread and general interest due to their strong language generation capabilities, offering great potential for bot…
Defending Our Privacy With Backdoors
Dominik Hintersdorf, Lukas Struppek, Daniel Neider +1
The proliferation of large AI models trained on uncurated, often sensitive web-scraped data has raised significant privacy concerns. One of the concerns is that adversaries can ext…
Be Careful What You Smooth For: Label Smoothing Can Be a Privacy Shield but Also a Catalyst for Model Inversion Attacks
Lukas Struppek, Dominik Hintersdorf, Kristian Kersting
Label smoothing -- using softened labels instead of hard ones -- is a widely adopted regularization method for deep learning, showing diverse benefits such as enhanced generalizati…