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[2602.13264] Directional Concentration Uncertainty: A representational approach to uncertainty quantification for generative models
Summary
The paper introduces Directional Concentration Uncertainty (DCU), a flexible framework for uncertainty quantification in generative models, outperforming traditional methods by utilizing the von Mises-Fisher distribution to measure geometric dispersion in generated outputs.
Why It Matters
As generative models become increasingly integral to AI applications, ensuring their reliability through effective uncertainty quantification is crucial. This research presents a novel approach that enhances the robustness and trustworthiness of these models, which is vital for their deployment in real-world scenarios.
Key Takeaways
- Directional Concentration Uncertainty (DCU) offers a new method for quantifying uncertainty in generative models.
- DCU utilizes the von Mises-Fisher distribution to measure the geometric dispersion of generated outputs.
- The proposed framework shows improved performance over existing heuristic methods in multi-modal tasks.
- DCU generalizes well across various tasks, enhancing the reliability of generative models.
- The research highlights the potential for integrating DCU into broader uncertainty quantification frameworks.
Computer Science > Machine Learning arXiv:2602.13264 (cs) [Submitted on 4 Feb 2026] Title:Directional Concentration Uncertainty: A representational approach to uncertainty quantification for generative models Authors:Souradeep Chattopadhyay, Brendan Kennedy, Sai Munikoti, Soumik Sarkar, Karl Pazdernik View a PDF of the paper titled Directional Concentration Uncertainty: A representational approach to uncertainty quantification for generative models, by Souradeep Chattopadhyay and 4 other authors View PDF HTML (experimental) Abstract:In the critical task of making generative models trustworthy and robust, methods for Uncertainty Quantification (UQ) have begun to show encouraging potential. However, many of these methods rely on rigid heuristics that fail to generalize across tasks and modalities. Here, we propose a novel framework for UQ that is highly flexible and approaches or surpasses the performance of prior heuristic methods. We introduce Directional Concentration Uncertainty (DCU), a novel statistical procedure for quantifying the concentration of embeddings based on the von Mises-Fisher (vMF) distribution. Our method captures uncertainty by measuring the geometric dispersion of multiple generated outputs from a language model using continuous embeddings of the generated outputs without any task specific heuristics. In our experiments, we show that DCU matches or exceeds calibration levels of prior works like semantic entropy (Kuhn et al., 2023) and also generalizes ...
