Generic Interpretation Approach for Transformer Models Incorporating Heterogenous Attention Structures
This paper proposes an interpretation method for Transformer models with heterogenous attention structures, including semantic and logical interpretation, validated through experiments.
Article intelligence
Key points
- Categorizes Transformer attention into homogenous and heterogenous types; heterogenous processes information from different sources.
- Proposes a generic interpretation method for heterogenous attention structures.
- Experiments include semantic and logical interpretation, demonstrating feasibility.
Why it matters
This matters because categorizes Transformer attention into homogenous and heterogenous types; heterogenous processes information from different sources.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.27458] Generic Interpretation Approach for Transformer Models Incorporating Heterogenous Attention Structures
[Submitted on 25 May 2026]
Title:Generic Interpretation Approach for Transformer Models Incorporating Heterogenous Attention Structures
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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.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2605.27458 [cs.CV]
(or arXiv:2605.27458v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.27458
arXiv-issued DOI via DataCite
Submission history
From: Yongjin Cui [view email] [v1] Mon, 25 May 2026 17:42:53 UTC (32,650 KB)
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