3 papers
cs.LG2026
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…
cs.CL2026
Uncertainty-Aware Generation and Decision-Making Under Ambiguity
Nico Daheim, Iryna Gurevych
With rapidly improving capabilities, Large Language Models (LLMs) are increasingly used in many complex real-world tasks. Beyond requiring in-depth knowledge and reasoning skills,…
cs.LG2026
Quantifying the Agreement Between Data-Influence and Data-Similarity to Understand LLM Behavior
Christopher J. Anders, Henrique Da Silva Gameiro, Nico Daheim +1
One way to understand LLM behavior is to trace its output back to the training data. Two types of measures are commonly used for output tracing: data-similarity and data-influence.…