9 papers
What Is a Clarification? Estimating Model-Dependent Input Ambiguity
Jingqin Tang, Tim Tomov, Sebastian Schmidt +2
Ambiguous inputs can admit multiple interpretations, each potentially yielding a different answer. Existing work largely treats this ambiguity as a property of the input, yet for a…
Task-Aware Calibration: Provably Optimal Decoding in LLMs
Tim Tomov, Dominik Fuchsgruber, Rajeev Verma +1
LLM decoding often relies on the model's predictive distribution to generate an output. Consequently, misalignment with respect to the true generating distribution leads to subopti…
Task-Awareness Improves LLM Generations and Uncertainty
Tim Tomov, Dominik Fuchsgruber, Stephan Günnemann
In many applications of LLMs, natural language responses often have an underlying structure such as representing discrete labels, numerical values, or graphs. Yet, existing decodin…
The Illusion of Certainty: Uncertainty Quantification for LLMs Fails under Ambiguity
Tim Tomov, Dominik Fuchsgruber, Tom Wollschläger +1
Accurate uncertainty quantification (UQ) in Large Language Models (LLMs) is critical for trustworthy deployment. While real-world language is inherently ambiguous, reflecting aleat…
Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theory
Dominik Fuchsgruber, Tom Wollschläger, Johannes Bordne +1
While uncertainty estimation for graphs recently gained traction, most methods rely on homophily and deteriorate in heterophilic settings. We address this by analyzing message pass…
Prior2Former -- Evidential Modeling of Mask Transformers for Assumption-Free Open-World Panoptic Segmentation
Sebastian Schmidt, Julius Körner, Dominik Fuchsgruber +3
In panoptic segmentation, individual instances must be separated within semantic classes. As state-of-the-art methods rely on a pre-defined set of classes, they struggle with novel…
Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariance
Dominik Fuchsgruber, Tim Poštuvan, Stephan Günnemann +1
Many applications in traffic, civil engineering, or electrical engineering revolve around edge-level signals. Such signals can be categorized as inherently directed, for example, t…
Energy-based Epistemic Uncertainty for Graph Neural Networks
Dominik Fuchsgruber, Tom Wollschläger, Stephan Günnemann
In domains with interdependent data, such as graphs, quantifying the epistemic uncertainty of a Graph Neural Network (GNN) is challenging as uncertainty can arise at different stru…
Uncertainty for Active Learning on Graphs
Dominik Fuchsgruber, Tom Wollschläger, Bertrand Charpentier +2
Uncertainty Sampling is an Active Learning strategy that aims to improve the data efficiency of machine learning models by iteratively acquiring labels of data points with the high…