Disentangling and Integrating Relational and Sensory Information in Transformer Architectures

2024-05-26

arXiv.org

AI Summary

Standard Transformers lack explicit mechanisms for modeling and routing relational structures among objects, limiting their performance on relational reasoning tasks. To address this, we propose the Dual Attention Transformer (DAT), the first Transformer architecture to explicitly decouple and jointly model sensory information (i.e., object attributes) and relational information (i.e., structured inter-object associations). DAT introduces two complementary attention mechanisms: sensory attention, which focuses on local attribute representations, and relational attention, a novel module designed to explicitly capture, aggregate, and propagate structured relational dependencies. We employ multi-task joint training and a unified evaluation framework across synthetic relational reasoning benchmarks as well as real-world language modeling and vision tasks. Experiments demonstrate substantial improvements in both data and parameter efficiency. Our results validate the effectiveness, generalizability, and architectural versatility of explicit relational modeling in Transformer-based architectures.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Statistical Relational/Logic Learning
• Computer Vision: Diffusion Models for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

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.

Problem

• Transformers lack explicit relational information processing mechanisms
• Separating sensory and relational information flow in neural architectures
• Improving data and parameter efficiency in relational reasoning tasks

Innovation

• Dual Attention Transformer with sensory and relational mechanisms
• Explicit relational computation enhances Transformer performance
• Separate attention for sensory and relational information flow