## Line Chart: Accuracy Comparison of Four AI Models on Math Topics ### Overview This image is a line chart comparing the performance (accuracy percentage) of four different large language models across a wide range of mathematical topics. The chart displays four distinct data series, each represented by a colored line with markers, plotted against a categorical x-axis of math skills and a numerical y-axis of accuracy. ### Components/Axes * **Chart Title:** Not explicitly stated. The content implies a title like "Model Accuracy on Math Benchmark Tasks." * **Y-Axis:** * **Label:** "Accuracy" * **Scale:** Linear scale from 20 to 100. * **Major Ticks:** 20, 30, 40, 50, 60, 70, 80, 90, 100. * **X-Axis:** * **Label:** Not explicitly labeled, but contains categorical data points for math topics. * **Categories (from left to right):** Angles, Area, Circles, Classifying & sorting, Coin names & value, Cones, Coordinate plane, Cubes, Cylinders, Decimals, Estimation & rounding, Exchanging money, Fractions, Light & heavy, Mixed operations, Multiple, Numerical exprs, Patterns, Perimeter, Place value, Powers, Rational number, Sphere, Spheres, Subtraction, Time, Triangles, Variable exprs, Volume of 3d shapes, Add, Compare, Count, Division, Equations, Length, Statistics, Percents, Polygons, Probability, Proportional, Quadrilaterals, Ratio, Temperature, Volume. * **Legend:** Positioned at the top center of the chart area. * **InternLM2-20B:** Blue line with circle markers. * **Yi-34B:** Orange line with diamond markers. * **Qwen-72B:** Green line with square markers. * **GPT-3.5:** Red line with triangle markers. ### Detailed Analysis **Trend Verification & Data Point Extraction (Approximate Values):** * **InternLM2-20B (Blue Line, Circles):** * **Trend:** Highly variable, generally the lowest-performing series. Shows sharp peaks and deep troughs. * **Key Points (Approx.):** Angles (~23%), Area (~63%), Circles (~53%), Classifying & sorting (~41%), Cubes (~50%), Decimals (~40%), Fractions (~59%), Mixed operations (~68%), Multiple (~67%), Numerical exprs (~55%), Patterns (~31%), Perimeter (~42%), Place value (~31%), Powers (~65%), Rational number (~70%), Sphere (~47%), Subtraction (~63%), Time (~50%), Triangles (~58%), Variable exprs (~50%), Volume of 3d shapes (~58%), Add (~45%), Compare (~39%), Count (~63%), Division (~45%), Equations (~75%), Length (~42%), Statistics (~35%), Percents (~50%), Polygons (~28%), Probability (~41%), Proportional (~58%), Quadrilaterals (~52%), Ratio (~70%). * **Yi-34B (Orange Line, Diamonds):** * **Trend:** Mid-to-high performance, often tracking closely with Qwen-72B but generally slightly below it and GPT-3.5. Shows significant volatility. * **Key Points (Approx.):** Angles (~59%), Area (~79%), Circles (~59%), Classifying & sorting (~76%), Coin names & value (~59%), Cones (~65%), Coordinate plane (~80%), Cubes (~48%), Cylinders (~45%), Decimals (~70%), Estimation & rounding (~82%), Exchanging money (~69%), Fractions (~90%), Light & heavy (~83%), Mixed operations (~95%), Multiple (~90%), Numerical exprs (~68%), Patterns (~63%), Perimeter (~56%), Place value (~79%), Powers (~59%), Rational number (~53%), Sphere (~75%), Subtraction (~89%), Time (~56%), Triangles (~85%), Variable exprs (~72%), Volume of 3d shapes (~69%), Add (~85%), Compare (~72%), Count (~70%), Division (~94%), Equations (~89%), Length (~78%), Statistics (~53%), Percents (~69%), Polygons (~88%), Probability (~84%), Proportional (~89%), Quadrilaterals (~84%), Ratio (~85%). * **Qwen-72B (Green Line, Squares):** * **Trend:** High performance, frequently the second-best series. Often follows a similar pattern to GPT-3.5 but at a slightly lower accuracy level. * **Key Points (Approx.):** Angles (~70%), Area (~47%), Circles (~82%), Classifying & sorting (~82%), Coin names & value (~70%), Cones (~65%), Coordinate plane (~72%), Cubes (~80%), Cylinders (~41%), Decimals (~45%), Estimation & rounding (~70%), Exchanging money (~50%), Fractions (~90%), Light & heavy (~83%), Mixed operations (~94%), Multiple (~90%), Numerical exprs (~79%), Patterns (~69%), Perimeter (~69%), Place value (~85%), Powers (~77%), Rational number (~77%), Sphere (~80%), Subtraction (~83%), Time (~84%), Triangles (~83%), Variable exprs (~95%), Volume of 3d shapes (~80%), Add (~78%), Compare (~69%), Count (~90%), Division (~85%), Equations (~84%), Length (~53%), Statistics (~69%), Percents (~89%), Polygons (~84%), Probability (~88%), Proportional (~89%), Quadrilaterals (~84%), Ratio (~85%). * **GPT-3.5 (Red Line, Triangles):** * **Trend:** Consistently the highest-performing series. Maintains high accuracy with less severe drops compared to other models. * **Key Points (Approx.):** Angles (~94%), Area (~79%), Circles (~59%), Classifying & sorting (~82%), Coin names & value (~70%), Cones (~83%), Coordinate plane (~95%), Cubes (~82%), Cylinders (~94%), Decimals (~75%), Estimation & rounding (~94%), Exchanging money (~78%), Fractions (~95%), Light & heavy (~100%), Mixed operations (~100%), Multiple (~84%), Numerical exprs (~84%), Patterns (~87%), Perimeter (~95%), Place value (~82%), Powers (~89%), Rational number (~85%), Sphere (~95%), Subtraction (~89%), Time (~95%), Triangles (~90%), Variable exprs (~100%), Volume of 3d shapes (~70%), Add (~94%), Compare (~79%), Count (~90%), Division (~95%), Equations (~82%), Length (~87%), Statistics (~56%), Percents (~88%), Polygons (~84%), Probability (~89%), Proportional (~95%), Quadrilaterals (~85%), Ratio (~95%). ### Key Observations 1. **Universal Difficulty:** All models show a significant performance dip on the "Angles" topic, with InternLM2-20B being the most affected (~23%). 2. **Universal Strength:** All models achieve very high accuracy (90-100%) on "Multiple" and "Numerical exprs" (with GPT-3.5 hitting 100% on both). 3. **Performance Hierarchy:** A clear and consistent hierarchy is visible: GPT-3.5 (Red) > Qwen-72B (Green) > Yi-34B (Orange) > InternLM2-20B (Blue) across the vast majority of topics. 4. **Volatility:** The InternLM2-20B series is the most volatile, with the largest swings between its highest and lowest points. 5. **Anomaly:** The "Statistics" topic shows a notable outlier where GPT-3.5's accuracy (~56%) drops significantly, falling below both Qwen-72B (~69%) and Yi-34B (~53% is lower, but the red line is clearly below green here). ### Interpretation This chart provides a comparative benchmark of mathematical reasoning capabilities across four AI models. The data suggests a strong correlation between model scale/complexity (implied by names like 72B vs. 20B) and performance on these tasks. GPT-3.5 demonstrates robust and leading performance, indicating superior generalization across diverse math problems. The consistent dips on topics like "Angles" and "Statistics" may point to inherent challenges in those areas for current language models, possibly due to the need for precise spatial reasoning or complex data interpretation. Conversely, the high scores on "Numerical exprs" and "Multiple" suggest these models are particularly adept at procedural arithmetic and multi-step calculation tasks. The chart is valuable for identifying specific strengths and weaknesses of each model. For instance, a user needing strong geometry performance might favor GPT-3.5 or Qwen-72B, while acknowledging that even top models struggle with certain concepts like "Angles." The performance gap between the largest model (GPT-3.5) and the smallest (InternLM2-20B) highlights the ongoing impact of model size and training on specialized reasoning tasks.