Explainable AI: Context-Aware Layer-Wise Integrated Gradients for Explaining Transformer Models
Researchers have developed a new framework called Context-Aware Layer-wise Integrated Gradients (CA-LIG) to improve the explainability of Transformer models. This framework offers a unified, hierarchical approach that computes layer-wise attributions and fuses them with attention gradients. CA-LIG aims to provide more faithful, context-sensitive, and semantically coherent explanations of how these models make decisions across various tasks and architectures. AI
IMPACT Provides more comprehensive and reliable explanations for Transformer decision-making, advancing interpretability.