## Line Graph: Model Accuracy Comparison Across Math Topics ### Overview The image is a multi-line graph comparing the accuracy of four AI models (InternLM2-Math-7B, InternLM2-7B, MAmmoTH-13B, WizardMath-13B) across 30+ math-related topics. Accuracy is measured on a 0-100% scale, with notable fluctuations across topics. ### Components/Axes - **X-axis**: Math topics (e.g., "Add & subtract," "Congruence & similarity," "Probability of simple events") - **Y-axis**: Accuracy percentage (0-100, increments of 20) - **Legend**: Top-left corner, color-coded: - Blue: InternLM2-Math-7B - Orange: InternLM2-7B - Green: MAmmoTH-13B - Red: WizardMath-13B ### Detailed Analysis 1. **InternLM2-Math-7B (Blue)**: - Consistently highest performer overall - Peaks at 95% in "Prime factorization" and "Polynomials" - Lowest point at 35% in "Radical expressions" - Average accuracy: ~65% 2. **InternLM2-7B (Orange)**: - Most erratic performance - Peaks at 70% in "Linear equations" - Drops to 5% in "Radical expressions" - Average accuracy: ~35% 3. **MAmmoTH-13B (Green)**: - High variability with extreme peaks/troughs - Reaches 90% in "Exponents & logarithms" - Drops to 20% in "Probability of simple events" - Average accuracy: ~55% 4. **WizardMath-13B (Red)**: - Most volatile performance - Spikes to 85% in "Square roots & cube roots" - Plummets to 0% in "Radical expressions" - Average accuracy: ~40% ### Key Observations - **Consistency**: InternLM2-Math-7B shows the most stable performance (standard deviation ~15%) - **Specialization**: All models struggle with "Radical expressions" (all <30%) - **Overperformance**: MAmmoTH-13B and WizardMath-13B show disproportionate peaks in "Probability" topics (up to 80%) - **Baseline**: InternLM2-7B underperforms across all topics compared to its larger counterparts ### Interpretation The data suggests: 1. **Model Specialization**: InternLM2-Math-7B's architecture is optimized for math tasks, evidenced by its consistent performance across diverse topics. 2. **Scaling Limitations**: InternLM2-7B's smaller size correlates with lower accuracy, particularly in complex topics. 3. **Overfitting Risks**: MAmmoTH-13B and WizardMath-13B show extreme variability, indicating potential overfitting to specific problem types. 4. **Knowledge Gaps**: All models struggle with radical expressions, suggesting a common limitation in current math AI systems. The graph reveals tradeoffs between model size, specialization, and generalization capabilities in mathematical reasoning tasks.