Directional Concentration Uncertainty: A representational approach to uncertainty quantification for generative models
arXiv:2602.13264v1 Announce Type: new 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 gener
arXiv:2602.13264v1 Announce Type: new 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 well to more complex tasks in multi-modal domains. We present a framework for the wider potential of DCU and its implications for integration into UQ for multi-modal and agentic frameworks.
Executive Summary
The article introduces Directional Concentration Uncertainty (DCU), a novel framework for Uncertainty Quantification (UQ) in generative models. DCU leverages the von Mises-Fisher (vMF) distribution to measure the geometric dispersion of embeddings from multiple generated outputs, providing a flexible and task-agnostic approach. The study demonstrates that DCU matches or surpasses the performance of existing heuristic methods, such as semantic entropy, and shows promise in multi-modal domains. The authors discuss the broader implications of DCU for enhancing the trustworthiness and robustness of generative models in various applications.
Key Points
- ▸ Introduction of DCU as a novel UQ framework for generative models.
- ▸ DCU uses the vMF distribution to quantify uncertainty through embedding dispersion.
- ▸ DCU outperforms or matches existing heuristic methods like semantic entropy.
- ▸ Generalization to multi-modal tasks and potential for integration into agentic frameworks.
Merits
Flexibility and Generalizability
DCU's task-agnostic nature allows it to be applied across various domains and modalities, making it highly versatile compared to rigid heuristic methods.
Performance
DCU demonstrates superior or comparable performance to existing methods, indicating its effectiveness in quantifying uncertainty.
Theoretical Rigor
The use of the vMF distribution provides a robust statistical foundation for measuring embedding dispersion, enhancing the reliability of the framework.
Demerits
Complexity
The statistical procedures involved in DCU may be complex, requiring specialized knowledge for implementation and interpretation.
Computational Overhead
The method may involve significant computational resources, particularly when dealing with large-scale or high-dimensional data.
Validation Scope
While the study shows promising results, further validation across a broader range of tasks and modalities is necessary to fully establish its generalizability.
Expert Commentary
The introduction of Directional Concentration Uncertainty (DCU) represents a significant advancement in the field of Uncertainty Quantification (UQ) for generative models. The framework's reliance on the von Mises-Fisher (vMF) distribution to measure embedding dispersion offers a statistically rigorous approach that is both flexible and generalizable. This is particularly noteworthy given the limitations of existing heuristic methods, which often fail to adapt to diverse tasks and modalities. The study's demonstration of DCU's superior or comparable performance to semantic entropy underscores its potential to become a standard method in UQ. However, the complexity of the statistical procedures and the computational overhead associated with DCU are notable limitations that may hinder its widespread adoption. Additionally, while the study provides promising results, further validation across a broader range of applications is essential to fully establish its robustness. The implications of DCU extend beyond technical advancements, offering practical benefits in enhancing the reliability of AI systems in critical sectors and informing policy decisions regarding AI deployment. Overall, DCU's innovative approach and potential for integration into multi-modal and agentic frameworks make it a valuable contribution to the ongoing efforts to make AI systems more trustworthy and robust.
Recommendations
- ✓ Conduct extensive validation studies across diverse tasks and modalities to further establish the generalizability of DCU.
- ✓ Develop user-friendly tools and resources to facilitate the implementation of DCU, particularly for practitioners with limited statistical expertise.
