Decoupling Knowledge and Reasoning in Transformers: A Modular Architecture with Generalized Cross-Attention

• URL: http://arxiv.org/abs/2501.00823v2
• Date: Mon, 06 Jan 2025 14:26:41 GMT
• Title: Decoupling Knowledge and Reasoning in Transformers: A Modular Architecture with Generalized Cross-Attention
• Authors: Zhenyu Guo, Wenguang Chen,
• Abstract summary: This paper introduces a novel modular Transformer architecture that explicitly decouples knowledge and reasoning. We provide a rigorous mathematical derivation demonstrating that the Feed-Forward Network (FFN) in a standard Transformer is a specialized case.
• Score: 9.401360346241296
• License:
• Abstract: Transformers have achieved remarkable success across diverse domains, but their monolithic architecture presents challenges in interpretability, adaptability, and scalability. This paper introduces a novel modular Transformer architecture that explicitly decouples knowledge and reasoning through a generalized cross-attention mechanism to a globally shared knowledge base with layer-specific transformations, specifically designed for effective knowledge retrieval. Critically, we provide a rigorous mathematical derivation demonstrating that the Feed-Forward Network (FFN) in a standard Transformer is a specialized case (a closure) of this generalized cross-attention, revealing its role in implicit knowledge retrieval and validating our design. This theoretical framework provides a new lens for understanding FFNs and lays the foundation for future research exploring enhanced interpretability, adaptability, and scalability, enabling richer interplay with external knowledge bases and other systems.

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