## Line Chart: Model Accuracy Across Mathematical Domains ### Overview The chart compares the accuracy performance of four large language models (LLMs) across 30+ mathematical domains. Models include Baichuan2-13B (blue), LLaMA2-13B (orange), Qwen-14B (green), and InternLM2-Math-20B (red). Accuracy is measured on a 0-100% scale, with notable volatility in performance across different mathematical topics. ### Components/Axes - **X-axis**: Mathematical domains (e.g., "Add & subtract," "Probability & statistics," "Polynomials") - **Y-axis**: Accuracy percentage (0-100, increments of 20) - **Legend**: Top-left corner with color-coded model identifiers - **Data series**: Four distinct lines with markers (circles for Baichuan2-13B, squares for LLaMA2-13B, diamonds for Qwen-14B, and triangles for InternLM2-Math-20B) ### Detailed Analysis 1. **Baichuan2-13B (Blue)**: - Average accuracy: ~65-75% - Notable peaks: "Exponents & scientific notation" (~85%), "Linear equations" (~80%) - Lowest performance: "Linear equations" (~50%) 2. **LLaMA2-13B (Orange)**: - Highest peak: "Prime factorization" (100%) - Sharp troughs: "Linear equations" (~10%), "Nonlinear functions" (~20%) - Average accuracy: ~50-60% 3. **Qwen-14B (Green)**: - Most consistent performance: ~40-50% across most domains - Lowest point: "Linear equations" (~0%) - Peaks: "Probability & statistics" (~60%), "Geometry" (~70%) 4. **InternLM2-Math-20B (Red)**: - Highest overall accuracy: ~80-90% in most domains - Peaks: "Probability & statistics" (~95%), "Polynomials" (~90%) - Lowest performance: "Linear equations" (~60%) ### Key Observations - **InternLM2-Math-20B** consistently outperforms others, particularly in advanced domains like "Probability & statistics" and "Polynomials." - **Qwen-14B** shows the most significant drop in "Linear equations" (near 0% accuracy). - **LLaMA2-13B** exhibits extreme volatility, with 100% accuracy in "Prime factorization" but near-zero in "Linear equations." - **Baichuan2-13B** demonstrates moderate performance with fewer extreme fluctuations. ### Interpretation The data suggests that InternLM2-Math-20B is optimized for mathematical reasoning, likely due to specialized training on mathematical datasets. Qwen-14B's poor performance in "Linear equations" may indicate a lack of focus on foundational algebraic concepts. LLaMA2-13B's volatility suggests inconsistent generalization across mathematical domains, while Baichuan2-13B shows balanced but suboptimal performance. The stark contrast in "Linear equations" accuracy across models highlights potential gaps in foundational mathematical training for some LLMs.