方向性集中不确定性：一种代表方法用于生成模型的不确定性量化
Directional Concentration Uncertainty: A representational approach to uncertainty quantification for generative models
AI总结：
本文提出方向性集中不确定性（DCU）方法，通过基于vMF分布的嵌入集中度量化，提升生成模型的不确定性量化性能，并在多模态任务中展现良好泛化能力。
摘要：
在使生成模型可信和稳健的关键任务中，不确定性量化（UQ）方法开始展现出令人鼓舞的潜力。然而，许多这些方法依赖于刚性的启发式方法，无法在不同任务和模态之间泛化。在这里，我们提出了一种新的UQ框架，具有高度的灵活性，并且在接近或超过先前启发式方法的性能方面取得了进展。我们引入了方向性集中不确定性（DCU），一种新的统计程序，用于根据von Mises-Fisher（vMF）分布来量化嵌入的集中程度。我们的方法通过使用生成输出的连续嵌入来测量语言模型生成的多个输出的几何分散性，从而捕捉不确定性，而无需任何特定任务的启发式方法。在我们的实验中，我们显示DCU与先前工作如语义熵（Kuhn等人，2023）的校准水平相匹配或超过，并且在多模态领域更复杂的任务中泛化效果良好。我们提出了DCU的更广泛潜力及其对多模态和代理框架中UQ整合的影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
