What Structural Inductive Bias Helps Transformers Reason Over Knowledge Graphs? A Study with Tabula RASA
arXiv:2602.02834v4 Announce Type: replace Abstract: What structural inductive bias helps transformers reason over knowledge graphs? Through controlled ablations of a minimal transformer modification with four independently removable components (sparse adjacency masking, edge-type biases, query scaling, value gating), we isolate which structural signals drive multi-hop reasoning. Our finding is sharp: sparse adjacency masking alone accounts for the dominant share of improvement over unmasked transformers (+72.5pp on 3-hop MetaQA, +45.5pp on WebQSP, +53.9pp on CWQ), while learned relation parame
The proliferation of transformer models necessitates ongoing research into improving their reasoning capabilities, particularly for complex data structures like knowledge graphs.
This research provides a clear, quantitative understanding of what structural inductive biases are most effective for transformers reasoning over knowledge graphs, directly impacting AI model efficiency and accuracy.
The identified 'sparse adjacency masking' becomes a critical architectural component for future transformer models designed for knowledge graph reasoning, improving performance significantly with less computational overhead.
- · AI researchers
- · Transformer model developers
- · Companies using knowledge graphs for AI applications
- · Inefficient transformer architectures
- · AI systems with poor multi-hop reasoning
Improved performance of AI systems that rely on knowledge graphs for complex reasoning tasks.
Accelerated development of more sophisticated AI applications in areas like question answering, drug discovery, and fraud detection.
Potentially enables new forms of 'agentic' AI that can navigate and synthesize information from vast, interconnected knowledge bases more effectively, contributing to the 'ai-agents' narrative.
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Read at arXiv cs.LG