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Train for Many, Update with One: Improving Multi-Sample Reasoning via Fenchel-Bregman Posterior Learning
Sungjun Lim, Adrian Robert Minut, Nico Carlsen Daheim +3
Multi-sample reasoning measures candidate coverage, the probability that at least one of candidates is correct. However, minimizing the average loss of individual attempts does…
SOAP-Bubbles: Structured Weight Uncertainty for Neural Networks
Adrian Robert Minut, Nico Daheim, Marco Miani +3
Structured weight-uncertainty can improve many aspects of deep learning, but it remains costly to estimate and difficult to implement. Here, we show that these issues can be addres…
Steering Vectors are an Adversarial Attack Surface
Abzal Aidakhmetov, Donato Crisostomi, Tommaso Mencattini +3
Activation steering has become a popular way to control Large Language Model (LLM) behavior without fine-tuning. Since the technique is plug-and-play, users share datasets and prec…
Mergenetic: a Simple Evolutionary Model Merging Library
Adrian Robert Minut, Tommaso Mencattini, Andrea Santilli +2
Model merging allows combining the capabilities of existing models into a new one - post hoc, without additional training. This has made it increasingly popular thanks to its low c…