## Bar Chart: Token Effectiveness by Average Accuracy ### Overview The chart compares the fraction of critical tokens and random tokens across two average accuracy thresholds (≤10% and >10%). Critical tokens (teal) and random tokens (light teal) are visualized with error bars indicating measurement uncertainty. ### Components/Axes - **X-axis**: "Average accuracy(%)" with two categories: - ≤10% (left) - >10% (right) - **Y-axis**: "Fraction(%)" ranging from 0 to 100% - **Legend**: - Teal: Critical tokens - Light teal: Random tokens - **Error bars**: Vertical lines atop bars representing ±2% (critical) and ±3% (random) uncertainty. ### Detailed Analysis 1. **≤10% Accuracy**: - Critical tokens: 88% ±2% - Random tokens: 55% ±3% 2. **>10% Accuracy**: - Critical tokens: 10% ±2% - Random tokens: 45% ±3% ### Key Observations - Critical tokens dominate at ≤10% accuracy (88% vs. 55%) but collapse to 10% at >10% accuracy. - Random tokens show a smaller drop (55% → 45%) across accuracy thresholds. - Error bars suggest higher variability in random token measurements (±3%) compared to critical tokens (±2%). ### Interpretation The data suggests critical tokens are disproportionately impactful at low accuracy thresholds but lose effectiveness as accuracy improves. Random tokens maintain a more stable presence, implying they may represent baseline or non-discriminative elements. The stark decline in critical token usage at >10% accuracy could indicate a shift in token relevance or model behavior under higher performance conditions. This pattern might reflect token selection biases in models trained on imbalanced datasets or tasks with varying difficulty levels.