Papers › Disentangling and Integrating Relational and Sensory Information in Transformer Architectures

Disentangling and Integrating Relational and Sensory Information in Transformer Architectures

26 May 2024arXiv:2405.16727archive 2025-07-28

Awni Altabaa, John Lafferty

Relational reasoning is a central component of generally intelligent systems, enabling robust and data-efficient inductive generalization. Recent empirical evidence shows that many existing neural architectures, including Transformers, struggle with tasks requiring relational reasoning. In this work, we distinguish between two types of information: sensory information about the properties of individual objects, and relational information about the relationships between objects. While neural attention provides a powerful mechanism for controlling the flow of sensory information between objects, the Transformer lacks an explicit computational mechanism for routing and processing relational information. To address this limitation, we propose an architectural extension of the Transformer framework that we call the Dual Attention Transformer (DAT), featuring two distinct attention mechanisms: sensory attention for directing the flow of sensory information, and a novel relational attention mechanism for directing the flow of relational information. We empirically evaluate DAT on a diverse set of tasks ranging from synthetic relational benchmarks to complex real-world tasks such as language modeling and visual processing. Our results demonstrate that integrating explicit relational computational mechanisms into the Transformer architecture leads to significant performance gains in terms of data efficiency and parameter efficiency.

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apply_rotary_emb awni00/dual-attention/dual_attention/dual_attention.py official repository ran · our draft was wrong MIT (permissive) · dd15a61ea3d0e604 · report
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DisentangledRelationalCrossAttention awni00/dual-attention/dual_attention/dual_attention.py official repository unverified MIT (permissive) · 65050ac2515e807a · report
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RelationalCrossAttention awni00/dual-attention/dual_attention/dual_attention.py official repository unverified MIT (permissive) · 8f6de9b1d93efd34 · report
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Tasks

Information RetrievalLanguage ModelingLanguage ModellingRelational ReasoningRetrieval

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Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSETSoftmaxTransformer

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