8 papers
Multi-Agent Systems are Mixtures of Experts: Who Becomes an Influencer?
Franka Bause, Jonas Niederle, Martin Pawelczyk +1
The effectiveness of multi-agent LLM deliberation depends not only on the agents' individual predictions, but also on how they communicate and collaborate. We study this mechanism…
Validity Threats for Foundation Model Research
Gunnar König, Martin Pawelczyk, Ulrike von Luxburg +1
Controlled experiments are the backbone of machine learning research, but at the scale of modern foundation models, they have become prohibitively expensive. Instead, the community…
Don't Trust Stubborn Neighbors: A Security Framework for Agentic Networks
Samira Abedini, Sina Mavali, Lea Schönherr +2
Large Language Model (LLM)-based Multi-Agent Systems (MASs) are increasingly deployed for agentic tasks, such as web automation, itinerary planning, and collaborative problem solvi…
Train Once, Answer All: Many Pretraining Experiments for the Cost of One
Sebastian Bordt, Martin Pawelczyk
Recent work has demonstrated that controlled pretraining experiments are a powerful tool for studying the relationship between training data and large language model (LLM) behavior…
Easy Data Unlearning Bench
Roy Rinberg, Pol Puigdemont, Martin Pawelczyk +1
Evaluating machine unlearning methods remains technically challenging, with recent benchmarks requiring complex setups and significant engineering overhead. We introduce a unified…
Machine Unlearning Fails to Remove Data Poisoning Attacks
Martin Pawelczyk, Jimmy Z. Di, Yiwei Lu +3
We revisit the efficacy of several practical methods for approximate machine unlearning developed for large-scale deep learning. In addition to complying with data deletion request…