Title: A Universal Framework for Interpreting Transformer Models with Heterogeneous Attention Mechanisms

Transformers have played a pivotal role in advancing artificial intelligence, particularly in the evolution of intelligent agents. This study classifies Transformer attention architectures into two distinct categories based on their input data origins: homogenous and heterogeneous. Heterogeneous attention structures, exemplified by co-attention mechanisms, are designed to process information derived from multiple, distinct sources. These structures serve as the cornerstone for enabling Transformers to execute sophisticated functions and assimilate diverse modalities of information.

Given both academic inquiry and regulatory mandates, interpreting Transformer models that utilize heterogeneous attention is a critical objective. The convergence of data from varied sources introduces unique interpretability challenges. Our research is divided into two primary components: methodological development and empirical validation. Methodologically, we introduce a novel interpretation framework tailored for Transformers employing heterogeneous attention. Empirically, utilizing our proposed analytical paradigm, we examine the operational mechanics of prominent models, providing both semantic and logical interpretations.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC