## Diagram: Query Path Templates for Training and Testing ### Overview The diagram illustrates hierarchical query path structures for training and testing templates, organized by maximum query path length (1-4 edges) and number of query paths (1-5). It uses color-coded nodes (blue=anchor, gray=variable, green=answer) and red brackets to group templates. ### Components/Axes - **Columns**: - Left: Max path length (1 or 2) - Middle: Max path length (2 or 3) - Right: Max path length (3 or 4) - **Rows**: Number of query paths (1-5, vertical axis) - **Legend**: - Blue: Anchor nodes - Gray: Variable nodes - Green: Answer nodes (answer-1/answer-2) - **Red Brackets**: - Left: Train query templates - Right: Test query templates ### Detailed Analysis 1. **Left Column (1-2 edges)**: - Paths contain 1-5 anchor → variable → answer sequences - Example: Row 3 shows 3 parallel paths with 2 edges each - All paths terminate in green answer nodes 2. **Middle Column (2-3 edges)**: - Paths include 1-5 anchor → variable → variable → answer sequences - Example: Row 4 shows 4 paths with 3 edges each - Gray nodes increase in number with path length 3. **Right Column (3-4 edges)**: - Paths contain 1-5 anchor → variable → variable → variable → answer sequences - Example: Row 5 shows 5 paths with 4 edges each - Gray nodes dominate longer paths 4. **Red Brackets**: - Train templates: Left column (1-2 edges) - Test templates: Right column (3-4 edges) - Middle column (2-3 edges) appears ungrouped ### Key Observations - Path complexity increases with maximum edge count - Longer paths (3-4 edges) show more variable nodes - Test templates exclusively use longest path structures - All paths maintain anchor → variable → answer flow - No overlapping node colors between anchor/variable/answer ### Interpretation This diagram demonstrates hierarchical query path design for NLP tasks: 1. **Training Focus**: Shorter paths (1-2 edges) likely capture basic relationships 2. **Testing Focus**: Longer paths (3-4 edges) test complex reasoning 3. **Variable Node Growth**: Path length correlates with variable node count 4. **Template Segregation**: Clear separation between training and testing structures 5. **Answer Node Consistency**: All paths terminate in green answer nodes regardless of length The structure suggests a progressive complexity model where training starts with simple queries and testing evaluates deeper reasoning capabilities. The color coding and bracket grouping emphasize systematic template organization for model evaluation.