## Line Chart: Separation between true and false statements across layers ### Overview The chart visualizes the relationship between between-class variance and within-class variance across 25 layers for four distinct categories: cities, neg_cities, sp_en_trans, and neg_sp_en_trans. The y-axis represents the ratio of between-class variance to within-class variance, while the x-axis represents sequential layers (0-25). Four colored lines track the variance patterns for each category. ### Components/Axes - **Title**: "Separation between true and false statements across layers" - **Y-axis**: "Between class variance / within-class variance" (ratio scale, 0.0–1.0) - **X-axis**: "Layer" (integer scale, 0–25) - **Legend**: Located in the top-right corner, with four entries: - Blue: cities - Orange: neg_cities - Green: sp_en_trans - Red: neg_sp_en_trans ### Detailed Analysis 1. **Cities (Blue Line)**: - Starts near 0.0 at layer 0. - Rises sharply to peak at ~0.8 between layers 10–12. - Declines gradually to ~0.2 by layer 25. 2. **Neg_cities (Orange Line)**: - Begins at 0.0, rises steeply to peak at ~1.0 at layer 12. - Drops sharply to ~0.1 by layer 25. 3. **Sp_en_trans (Green Line)**: - Starts at 0.0, rises to peak at ~0.4 between layers 10–12. - Declines to ~0.1 by layer 25. 4. **Neg_sp_en_trans (Red Line)**: - Begins at 0.0, rises to peak at ~0.6 between layers 12–14. - Declines to ~0.2 by layer 25. ### Key Observations - All lines exhibit a "peak-and-decline" pattern, with maximum separation occurring between layers 10–14. - **Neg_cities** achieves the highest peak (~1.0), suggesting extreme separation between true/false statements in these layers. - **Neg_sp_en_trans** maintains the highest separation after layer 20 (~0.2), outperforming other categories in later layers. - **Cities** and **sp_en_trans** show similar peak magnitudes (~0.8 and ~0.4, respectively), but **cities** declines more gradually. ### Interpretation The data suggests that model performance in distinguishing true/false statements varies significantly across categories and layers. The **neg_cities** category demonstrates the strongest separation (highest between-class variance) during layers 10–12, potentially indicating optimal model behavior for this group. The decline after layer 14 across all categories may reflect overfitting or diminishing returns. Notably, **neg_sp_en_trans** maintains higher separation than **sp_en_trans** in later layers, implying structural differences in how these categories are processed. The gradual decline of **cities** suggests sustained but diminishing effectiveness in separating statements as layers increase.