Integrating attention into explanation frameworks for language and vision transformers

arXiv:2508.08966 · cs.LG, stat.ML · Submitted 2025-08-12 · Read on arXiv

cs.LG, stat.ML

Submitted: 2025-08-12

Updated: 2026-08-29

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Transformer — 51× in this paper · explained in 2 episodes
• Shapley values — 26× in this paper · explained in 2 episodes
• Attention Mechanism — 17× in this paper · explained in 2 episodes
• Explainability — 8× in this paper · explained in 3 episodes
• Transformer Architecture — 7× in this paper · explained in 2 episodes
• F1 Score — 6× in this paper · explained in 3 episodes
• Transformer Models — 6× in this paper · explained in 2 episodes
• Latent Representation — 5× in this paper · explained in 2 episodes
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• Explainable AI (XAI) — 3× in this paper · explained in 7 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• SHAP values — 3× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Vector Representations — 1× in this paper · explained in 2 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP tasks
• Improving the Weighting Strategy in KernelSHAP
• ASCENT-ViT: Attention-based Scale-aware Concept Learning Framework for Enhanced Alignment in Vision Transformers

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