Abstract
Quantitatively explaining the strength of arguments under gradual semantics has recently received increasing attention. Specifically, several works in the literature provide quantitative explanations by computing the attribution scores of arguments. These works disregard the importance of attacks and supports, even though they play an essential role when explaining arguments' strength. In this paper, we propose a novel theory of Relation Attribution Explanations (RAEs), adapting Shapley values from game theory to offer fine-grained insights into the role of attacks and supports in quantitative bipolar argumentation towards obtaining the arguments' strength. We show that RAEs satisfy several desirable properties. We also propose a probabilistic algorithm to approximate RAEs efficiently. Finally, we show the application value of RAEs in fraud detection and large language models case studies.
Abstract (translated)
量化解释在逐渐语义下论证的力量近年来受到了越来越多的关注。具体来说，许多文献通过计算论证的归因分数来提供定量的解释。尽管这些工作忽略了攻击和支撑的重要性，尽管它们在解释论证的力量方面发挥着至关重要的作用，但这些工作仍然忽略了这一点。在本文中，我们提出了一个名为关系归因解释理论（RAEs）的新理论，从博弈理论中借鉴Shapley值，以提供对攻击和支持在定量二分论证中获得论证力量的重要性的深入理解。我们证明了RAEs满足多个有用的属性。我们还提出了一个高效的概率算法来近似RAEs。最后，我们在欺诈检测和大语言模型案例研究中展示了RAEs的应用价值。
URL
https://arxiv.org/abs/2404.14304
