Explainable AI: Context-Aware Layer-Wise Integrated Gradients for Explaining Transformer Models
arXiv:2602.16608v2 Announce Type: replace-cross Abstract: Transformer models achieve state-of-the-art performance across domains and tasks, yet their deeply layered representations make their predictions difficult to interpret. Existing explainability methods rely on final-layer attributions, capture either local token-level attributions or global attention patterns without unification, and lack context-awareness of inter-token dependencies and structural components. They also fail to capture how relevance evolves across layers and how structural components shape decision-making. To address th
The increasing complexity and widespread deployment of transformer models necessitate robust explainability to ensure trust, refine development, and meet regulatory demands.
Improved explainability for state-of-the-art AI models will enhance their reliability, foster responsible AI development, and accelerate their adoption in critical applications by demystifying their decision-making processes.
The proposed method of context-aware, layer-wise integrated gradients offers a more comprehensive way to understand how transformer models arrive at their predictions, moving beyond final-layer or isolated attention analyses.
- · AI researchers
- · Developers of AI systems
- · Industries deploying complex AI
- · Regulatory bodies
- · Companies with opaque AI models
- · Simplistic explainability methods
This research directly advances the field of Explainable AI by introducing a novel technique for interpreting transformer models.
Improved explainability could lead to more robust, ethical, and deployable AI systems, enhancing trust and accelerating enterprise adoption.
Enhanced interpretability may reduce the 'black box' problem, potentially slowing calls for strict, prohibitive AI regulations by providing clearer insights into model behavior.
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