Explainable AI: Context-Aware Layer-Wise Integrated Gradients for Explaining Transformer Models

2026-02-18 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing, Computer Vision · Depth: Expert, medium

Summary

Jugal Kalita and Melkamu Abay Mersha introduced the Context-Aware Layer-wise Integrated Gradients (CA-LIG) Framework, a new explainable AI method designed to interpret Transformer models. Existing methods often rely on final-layer attributions, lack context-awareness, and fail to capture how relevance evolves across layers. CA-LIG addresses these limitations by computing layer-wise Integrated Gradients within each Transformer block and fusing these token-level attributions with class-specific attention gradients. This approach generates signed, context-sensitive attribution maps that show supportive and opposing evidence, tracing the hierarchical flow of relevance. The framework was evaluated across diverse tasks, domains, and Transformer families, including sentiment analysis with BERT, hate speech detection with XLM-R and AfroLM, and image classification with Masked Autoencoder vision Transformer models. CA-LIG consistently provided more faithful, context-sensitive, and semantically coherent explanations compared to established methods.

Key takeaway

For research scientists working with Transformer models, understanding internal decision-making is critical. You should consider adopting the CA-LIG Framework to gain more faithful and context-aware explanations of model predictions. This framework offers a unified, hierarchical view of relevance flow, which can significantly improve the interpretability and conceptual understanding of your deep neural models across various tasks and architectures.

Key insights

CA-LIG provides context-aware, layer-wise explanations for Transformer models by integrating token attributions with attention gradients.

Principles

• Relevance evolves hierarchically across layers.
• Context-awareness is crucial for accurate attributions.

Method

CA-LIG computes layer-wise Integrated Gradients within Transformer blocks, then fuses token-level attributions with class-specific attention gradients to create signed, context-sensitive attribution maps.

In practice

• Apply CA-LIG for sentiment analysis.
• Use CA-LIG for hate speech detection.
• Employ CA-LIG for image classification.

Topics

• Explainable AI
• Transformer Models
• Integrated Gradients
• Attribution Frameworks
• Model Interpretability

Best for: Research Scientist, AI Researcher, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.