Attention Based Interpretation

Attention-based interpretation seeks to understand how neural network models, particularly Transformers, arrive at their predictions by analyzing their internal attention mechanisms. Current research focuses on developing methods to extract meaningful explanations from attention weights, often comparing these explanations to human reasoning or ground truth importance scores, and exploring techniques to improve the faithfulness and efficiency of these interpretations. This work is crucial for building trust in complex models used in high-stakes applications, such as those impacting human lives, and for advancing our understanding of how these models process information.