Quantifying the Contributions of Attacks and Supports in Bipolar Argumentation Frameworks

Core Concepts

Relation Attribution Explanations (RAEs) quantify the contribution of attacks and supports in Quantitative Bipolar Argumentation Frameworks (QBAFs) to explain the strength of arguments.

Abstract

The paper proposes a novel theory of Relation Attribution Explanations (RAEs) to offer fine-grained insights into the role of attacks and supports in quantitative bipolar argumentation. RAEs are based on Shapley values from game theory and aim to explain the strength of a topic argument by quantifying the contribution of each edge (attack or support relation) in the QBAF.
The key highlights and insights are:

RAEs satisfy several desirable properties adapted from Shapley value properties, such as Efficiency, Dummy, Symmetry, and Dominance. They also introduce new argumentative properties like Sign Correctness, Counterfactuality, Qualitative Invariability, and Quantitative Variability.

The satisfaction and violation of these properties theoretically show that RAEs provide reasonable and faithful explanations, which is crucial for explanation methods.

The authors propose a probabilistic algorithm to efficiently approximate RAEs, prove theoretical convergence guarantees, and demonstrate experimentally that it converges quickly.

Two case studies are carried out to evaluate and demonstrate the practical usefulness of RAEs in fraud detection and large language model explanation tasks. The case studies illustrate how RAEs can provide more fine-grained insights compared to argument-based attribution explanations.

Overall, the paper introduces a novel and principled approach to explain the strength of arguments in QBAFs by considering the contributions of individual attacks and supports.

Stats

The base scores of all arguments in the QBAF example are set to 0.5.
Under the DF-QuAD gradual semantics, the strength of the topic argument α is 0.8046875.

Quotes

"Relation Attribution Explanations (RAEs) look at every subset of edges (S ⊆ R) and compute the marginal contribution of r (σS∪{r}(α) - σS(α))."
"RAEs satisfy several desirable properties adapted from Shapley value properties, such as Efficiency, Dummy, Symmetry, and Dominance."
"The satisfaction and violation of these properties theoretically show that RAEs provide reasonable and faithful explanations, which is crucial for explanation methods."

Key Insights Distilled From

Explaining Arguments' Strength: Unveiling the Role of Attacks and Supports (Technical Report)

by Xiang Yin,Po... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.14304.pdf

Deeper Inquiries

How can RAEs be extended to handle edge-weighted QBAFs?

To extend Relation Attribution Explanations (RAEs) to handle edge-weighted Quantitative Bipolar Argumentation Frameworks (QBAFs), we need to incorporate the weights of the edges into the computation of the contributions. In edge-weighted QBAFs, each edge has a weight that represents the strength of the attack or support relationship between arguments.
One approach to handle edge-weighted QBAFs with RAEs is to modify the calculation of the marginal contribution of an edge to the topic argument by taking into account the edge weights. The weight of an edge can be used to scale the contribution of that edge to the overall strength of the topic argument. By incorporating edge weights into the RAE calculation, we can provide more nuanced and accurate explanations of the impact of each edge on the final strength of the topic argument.

How do RAEs compare to other argument-based attribution explanations in terms of computational complexity and practical applicability?

RAEs offer a more fine-grained and comprehensive insight into the role of attacks and supports in explaining the strength of arguments compared to other argument-based attribution explanations. In terms of computational complexity, RAEs can be more computationally intensive, especially when computing them exactly for large QBAFs. However, the probabilistic approximation algorithm for RAEs provides a more efficient way to compute RAEs, making them practical for real-world applications.
In practical applicability, RAEs excel in scenarios where a deeper understanding of the reasoning process is required. They provide a more detailed analysis of how different edges contribute to the overall strength of the topic argument, offering a more nuanced and insightful explanation. This can be particularly valuable in domains where the reasoning process is complex and requires a thorough explanation.

What are the potential limitations of using RAEs for explaining the reasoning of large language models, and how can these be addressed?

One potential limitation of using RAEs for explaining the reasoning of large language models is the scalability issue when dealing with very large QBAFs. As the size of the QBAF increases, the computational complexity of computing RAEs grows, making it challenging to provide real-time explanations for complex models.
To address this limitation, one approach is to optimize the probabilistic approximation algorithm for RAEs to improve its efficiency for large-scale QBAFs. This could involve refining the sampling strategy or leveraging parallel computing techniques to speed up the computation process. Additionally, developing specialized algorithms or heuristics tailored for large language models can help streamline the explanation process and make it more feasible for practical applications.