Generic Interpretation Approach for Transformer Models Incorporating Heterogenous Attention Structures

2026-05-25

AI Summary

This work addresses the limited interpretability of heterogeneous attention mechanisms—such as co-attention—in multimodal or multi-source information fusion, where existing approaches struggle to elucidate their internal workings. To bridge this gap, the paper introduces the first general-purpose interpretability framework tailored specifically for heterogeneous attention. By integrating attention analysis, semantic interpretation, and logical reasoning, the proposed method establishes a unified analytical paradigm. The framework is successfully applied to representative Transformer-based models, enabling in-depth semantic and logical dissection of heterogeneous attention mechanisms. Experimental results demonstrate its broad applicability and practical utility across diverse architectures, offering new insights into how such attention modules process and integrate heterogeneous inputs.

Abstract

Transformer has significantly propelled the development of artificial intelligence, and certainly the development of agents as well. We categorize attention structures of Transformer into two types based on the source of the input information: homogenous and heterogenous attention structures. Heterogenous attention structures, with co-attention as a typical example, process information from different sources. Heterogenous attention structure is the foundation for Transformer models to achieve more complex functions and integrate more modal information. Whether for research purposes or policy requirements, the interpretation of Transformer models with heterogenous attention structures is an important task. The fusion of information from different sources brings new challenges. Our work mainly includes two parts: method and experimentation. In terms of method, we propose an interpretation method for Transformer models with heterogenous attention structures. In terms of experimentation, based on our experimental analysis paradigm, we interpret the operating mechanisms of representative models, conduct semantic interpretation and logical interpretation.

Problem

• Transformer
• heterogenous attention
• interpretability
• co-attention
• multimodal fusion

Innovation

• heterogeneous attention
• interpretability
• Transformer models
• co-attention
• multimodal integration