## Line Chart: Model Accuracy Comparison ### Overview The image is a line chart comparing the mean accuracy of various language models on English and Chinese datasets, as well as their overall performance. The x-axis represents different language models, and the y-axis represents the mean accuracy, ranging from 20 to 90. The chart includes three data series: English (blue dashed line), Chinese (green dashed line), and Overall (black solid line). ### Components/Axes * **Title:** None * **X-axis:** Language Models (WizardMath-13B, LLaMA2-7B, MetaMath-13B, LLaMA2-13B, LLaMA2-70B, MAmmoTH-13B, Yi-6B, ChatGLM3-6B, Baichuan2-13B, InternLM2-7B, Qwen-14B, InternLM2-20B, Yi-34B, InternLM2-Math-7B, DeepSeekMath-7B, InternLM2-Math-20B, GPT-3.5, Qwen-72B, GPT-4) * **Y-axis:** Mean Accuracy, ranging from 20 to 90 in increments of 10. * **Legend:** Located in the top-left corner. * English: Blue dashed line with circular markers * Chinese: Green dashed line with circular markers * Overall: Black solid line with circular markers ### Detailed Analysis **English (Blue Dashed Line):** The English accuracy fluctuates more than the other two lines. * WizardMath-13B: ~40 * LLaMA2-7B: ~38 * MetaMath-13B: ~50 * LLaMA2-13B: ~47 * LLaMA2-70B: ~65 * MAmmoTH-13B: ~57 * Yi-6B: ~65 * ChatGLM3-6B: Not Available * Baichuan2-13B: Not Available * InternLM2-7B: ~62 * Qwen-14B: ~69 * InternLM2-20B: ~69 * Yi-34B: ~70 * InternLM2-Math-7B: Not Available * DeepSeekMath-7B: Not Available * InternLM2-Math-20B: ~88 * GPT-3.5: ~75 * Qwen-72B: ~89 * GPT-4: Not Available **Chinese (Green Dashed Line):** The Chinese accuracy generally increases across the models. * WizardMath-13B: ~22 * LLaMA2-7B: ~27 * MetaMath-13B: ~32 * LLaMA2-13B: ~34 * LLaMA2-70B: ~42 * MAmmoTH-13B: ~48 * Yi-6B: ~48 * ChatGLM3-6B: Not Available * Baichuan2-13B: Not Available * InternLM2-7B: ~54 * Qwen-14B: ~52 * InternLM2-20B: ~52 * Yi-34B: ~58 * InternLM2-Math-7B: Not Available * DeepSeekMath-7B: Not Available * InternLM2-Math-20B: ~62 * GPT-3.5: ~67 * Qwen-72B: ~70 * GPT-4: ~73 **Overall (Black Solid Line):** The overall accuracy shows a general upward trend. * WizardMath-13B: ~28 * LLaMA2-7B: ~31 * MetaMath-13B: ~36 * LLaMA2-13B: ~40 * LLaMA2-70B: ~50 * MAmmoTH-13B: ~50 * Yi-6B: ~54 * ChatGLM3-6B: Not Available * Baichuan2-13B: Not Available * InternLM2-7B: ~54 * Qwen-14B: ~57 * InternLM2-20B: ~64 * Yi-34B: ~60 * InternLM2-Math-7B: Not Available * DeepSeekMath-7B: Not Available * InternLM2-Math-20B: ~71 * GPT-3.5: ~71 * Qwen-72B: ~71 * GPT-4: ~78 ### Key Observations * The English accuracy fluctuates more significantly than the Chinese and Overall accuracies. * The Overall accuracy generally increases as the models progress along the x-axis. * The Chinese accuracy consistently lags behind the English accuracy for most models. * The models on the right side of the chart (GPT-3.5, Qwen-72B, GPT-4) generally exhibit higher accuracy across all three metrics. ### Interpretation The chart illustrates the performance of various language models on English and Chinese datasets. The fluctuating English accuracy suggests that some models are better suited for English tasks than others, while the consistently lower Chinese accuracy indicates a potential gap in model performance across different languages. The upward trend in overall accuracy suggests that newer models generally perform better than older models. The models GPT-3.5, Qwen-72B, and GPT-4 show the highest overall performance, indicating their superior capabilities in both English and Chinese language tasks. The data suggests that model architecture and training data play a significant role in determining language-specific performance.

## Line Chart: Mean Accuracy of Language Models ### Overview This line chart displays the mean accuracy of several language models on tasks in English, Chinese, and overall. The x-axis represents different language models, and the y-axis represents the mean accuracy, ranging from approximately 20 to 90. Three distinct lines represent the performance in each language/overall. ### Components/Axes * **X-axis:** Language Model (WizardMath-13B, LLaMA2-7B, MetaMath-13B, LLaMA2-13B, LLaMA2-70B, MAmmoTH-13B, Yi-6B, ChatGLM3-6B, Baichuan2-13B, InternLM2-7B, Qwen-14B, InternLM2-20B, Yi-34B, InternLM2-Math-7B, DeepSeekMath-7B, InternLM2-Math-20B, GPT-3.5, Qwen-72B, GPT-4) * **Y-axis:** Mean Accuracy (Scale from approximately 20 to 90) * **Legend:** * Blue dashed line: English * Green dotted line: Chinese * Black solid line: Overall ### Detailed Analysis The chart shows the accuracy of different language models across English, Chinese, and overall performance. * **WizardMath-13B:** English: ~38, Chinese: ~24, Overall: ~32 * **LLaMA2-7B:** English: ~40, Chinese: ~28, Overall: ~35 * **MetaMath-13B:** English: ~42, Chinese: ~30, Overall: ~37 * **LLaMA2-13B:** English: ~45, Chinese: ~33, Overall: ~40 * **LLaMA2-70B:** English: ~55, Chinese: ~40, Overall: ~50 * **MMoTH-13B:** English: ~58, Chinese: ~45, Overall: ~53 * **Yi-6B:** English: ~48, Chinese: ~38, Overall: ~44 * **ChatGLM3-6B:** English: ~52, Chinese: ~42, Overall: ~48 * **Baichuan2-13B:** English: ~55, Chinese: ~45, Overall: ~50 * **InternLM2-7B:** English: ~58, Chinese: ~48, Overall: ~54 * **Qwen-14B:** English: ~62, Chinese: ~52, Overall: ~58 * **InternLM2-20B:** English: ~65, Chinese: ~55, Overall: ~61 * **Yi-34B:** English: ~68, Chinese: ~58, Overall: ~64 * **InternLM2-Math-7B:** English: ~66, Chinese: ~56, Overall: ~62 * **DeepSeekMath-7B:** English: ~70, Chinese: ~60, Overall: ~66 * **InternLM2-Math-20B:** English: ~72, Chinese: ~62, Overall: ~68 * **GPT-3.5:** English: ~78, Chinese: ~68, Overall: ~74 * **Qwen-72B:** English: ~85, Chinese: ~75, Overall: ~81 * **GPT-4:** English: ~88, Chinese: ~78, Overall: ~84 **Trends:** * **English:** The English accuracy line generally slopes upward, with a steeper increase towards the end of the model list. It starts around 38 and reaches approximately 88. * **Chinese:** The Chinese accuracy line also slopes upward, but is consistently lower than the English line. It starts around 24 and reaches approximately 78. * **Overall:** The overall accuracy line generally follows the English line, starting around 32 and reaching approximately 84. ### Key Observations * GPT-4 consistently demonstrates the highest overall accuracy. * English accuracy is generally higher than Chinese accuracy across all models. * The gap between English and Chinese accuracy appears to narrow for some models (e.g., Qwen-72B, GPT-4). * There are some fluctuations in accuracy for certain models, indicating potential variations in performance. ### Interpretation The data suggests that language models are generally more accurate on English tasks than on Chinese tasks. This could be due to a variety of factors, including the availability of more training data in English, the complexity of the Chinese language, or the specific tasks used for evaluation. The increasing trend in accuracy across the model list indicates that language model performance is improving over time. The performance of GPT-4 and Qwen-72B suggests that larger models with more parameters are capable of achieving higher accuracy. The narrowing gap between English and Chinese accuracy for the most advanced models suggests that these models are becoming more proficient in handling both languages. The fluctuations in accuracy for certain models may indicate that their performance is sensitive to the specific task or dataset used for evaluation. This chart provides a comparative analysis of language model performance, highlighting the strengths and weaknesses of different models in different languages.

## Line Chart: Mean Accuracy of Language Models by Language ### Overview This image is a line chart comparing the mean accuracy of various large language models (LLMs) across three categories: English, Chinese, and an Overall score. The chart plots model names on the x-axis against a "Mean Accuracy" score (ranging from 20 to 90) on the y-axis. The data suggests a general upward trend in model performance from left to right, with English performance consistently outperforming Chinese performance for most models. ### Components/Axes * **Chart Type:** Multi-series line chart with markers. * **Y-Axis:** * **Title:** "Mean Accuracy" * **Scale:** Linear, from 20 to 90, with major gridlines at intervals of 10. * **X-Axis:** * **Labels (from left to right):** WizardMath-13B, LLaMA2-7B, MetaMath-13B, LLaMA2-13B, LLaMA2-70B, MAmmoTH-13B, Yi-6B, ChatGLM3-6B, Baichuan2-13B, InternLM2-7B, Qwen-14B, InternLM2-20B, Yi-34B, InternLM2-Math-7B, DeepSeekMath-7B, InternLM2-Math-20B, GPT-3.5, Qwen-72B, GPT-4. * **Legend (Top-Left Corner):** * **Blue dashed line with circle markers:** "English" * **Green dashed line with circle markers:** "Chinese" * **Black solid line with circle markers:** "Overall" ### Detailed Analysis The following table reconstructs the approximate data points for each model, based on visual inspection of the chart. Values are estimated to the nearest integer. | Model Name | English Accuracy (Blue) | Chinese Accuracy (Green) | Overall Accuracy (Black) | | :--- | :--- | :--- | :--- | | WizardMath-13B | ~40 | ~22 | ~28 | | LLaMA2-7B | ~38 | ~27 | ~31 | | MetaMath-13B | ~50 | ~28 | ~35 | | LLaMA2-13B | ~47 | ~31 | ~36 | | LLaMA2-70B | ~51 | ~34 | ~40 | | MAmmoTH-13B | ~56 | ~34 | ~41 | | Yi-6B | ~65 | ~42 | ~50 | | ChatGLM3-6B | ~57 | ~46 | ~50 | | Baichuan2-13B | ~65 | ~48 | ~54 | | InternLM2-7B | ~61 | ~51 | ~54 | | Qwen-14B | ~56 | ~54 | ~55 | | InternLM2-20B | ~61 | ~55 | ~57 | | Yi-34B | ~69 | ~51 | ~58 | | InternLM2-Math-7B | ~69 | ~58 | ~61 | | DeepSeekMath-7B | ~69 | ~62 | ~64 | | InternLM2-Math-20B | ~72 | ~62 | ~65 | | GPT-3.5 | ~86 | ~63 | ~70 | | Qwen-72B | ~76 | ~68 | ~71 | | GPT-4 | ~88 | ~73 | ~78 | **Trend Verification:** * **English (Blue Dashed Line):** Shows a general upward trend with significant volatility. Notable peaks occur at Yi-6B, Baichuan2-13B, and a very sharp increase at GPT-3.5 and GPT-4. There are dips at LLaMA2-7B, LLaMA2-13B, ChatGLM3-6B, and Qwen-14B. * **Chinese (Green Dashed Line):** Shows a steadier, more consistent upward trend with fewer sharp fluctuations. The growth is relatively smooth from WizardMath-13B to GPT-4. * **Overall (Black Solid Line):** Follows a smooth, consistent upward trajectory that generally lies between the English and Chinese lines, acting as an average. It shows very few dips. ### Key Observations 1. **Performance Hierarchy:** For nearly every model, the English accuracy score is higher than the Chinese score, which is in turn higher than or equal to the Overall score. The Overall line is a composite metric. 2. **Model Progression:** There is a clear, general trend of increasing accuracy from older/smaller models on the left (e.g., WizardMath-13B) to newer/larger models on the right (e.g., GPT-4). 3. **Significant Outliers:** * **GPT-3.5** shows a dramatic spike in English accuracy (~86), far above its Chinese (~63) and Overall (~70) scores, creating the largest gap between English and Chinese performance on the chart. * **Yi-34B** exhibits a notable dip in Chinese accuracy (~51) compared to its neighbors, while its English accuracy remains high (~69). * **Qwen-14B** is an instance where the Chinese accuracy (~54) nearly matches the English accuracy (~56), showing one of the smallest language performance gaps. 4. **Top Performers:** GPT-4 leads in all three categories (English: ~88, Chinese: ~73, Overall: ~78). Qwen-72B and GPT-3.5 follow in the overall ranking. ### Interpretation This chart visualizes the progression and comparative capabilities of LLMs, likely on a mathematical or reasoning benchmark given the model names (e.g., "Math"). The data demonstrates two key findings: 1. **The English Advantage:** Most models, especially Western-developed ones like GPT-3.5, show a significant performance advantage in English over Chinese. This suggests potential biases in training data or architectural optimizations favoring English-language tasks. 2. **The Closing Gap:** Newer models, particularly those from Chinese labs (e.g., Qwen-72B, InternLM2-Math-20B), show a narrowing gap between English and Chinese performance. This indicates successful efforts in improving multilingual capabilities, especially for Chinese. 3. **Overall Trend:** The consistent rise of the black "Overall" line signifies broad, across-the-board improvements in model capabilities over successive generations. The volatility in the English line suggests that gains in English performance may be less stable or more sensitive to specific model architectures and training techniques compared to the steadier improvements in Chinese performance. The chart serves as a benchmark snapshot, highlighting both the current state-of-the-art (GPT-4) and the dynamic landscape of LLM development, where language-specific performance remains a critical differentiator.

## Line Graph: Mean Accuracy Comparison Across Language Models ### Overview The image displays a line graph comparing mean accuracy performance across multiple language models (LMs) for English, Chinese, and overall metrics. The graph spans 20-90% accuracy on the y-axis and lists 18 models on the x-axis, ranging from WizardMath-13B to GPT-4. ### Components/Axes - **X-axis**: Model names (e.g., WizardMath-13B, LLaMA2-7B, Yi-34B, GPT-4) - **Y-axis**: Mean Accuracy (20-90% in 10% increments) - **Legend**: - Blue dashed line: English - Green dash-dot line: Chinese - Black solid line: Overall - **Placement**: Legend in top-left corner; data points connected by lines with markers ### Detailed Analysis 1. **English (Blue Dashed Line)**: - Starts at ~40% (WizardMath-13B), peaks at ~88% (GPT-4) - Notable dip to ~55% at Yi-34B, then sharp rise to 88% at GPT-4 - Average accuracy: ~65% (excluding GPT-4 outlier) 2. **Chinese (Green Dash-Dot Line)**: - Begins at ~22% (WizardMath-13B), rises steadily to ~73% (GPT-4) - Sharp dip to ~51% at InternLM2-7B, then recovery to 73% - Average accuracy: ~55% (excluding GPT-4 outlier) 3. **Overall (Black Solid Line)**: - Starts at ~28% (WizardMath-13B), climbs to ~78% (GPT-4) - Consistent upward trend with minor fluctuations - Average accuracy: ~55% (excluding GPT-4 outlier) ### Key Observations - **Performance Gaps**: English models consistently outperform Chinese models by 10-15% across most models - **Outliers**: - GPT-4 shows extreme performance (88% English, 73% Chinese) - Yi-34B causes English accuracy drop to 55% - InternLM2-7B causes Chinese accuracy drop to 51% - **Trend Patterns**: - English: Volatile with high peaks - Chinese: Steady growth with mid-range dip - Overall: Smooth progression with minor fluctuations ### Interpretation The data suggests English language models generally achieve higher accuracy than Chinese models, with GPT-4 demonstrating exceptional performance across both languages. The dips observed in Yi-34B (English) and InternLM2-7B (Chinese) indicate potential model-specific limitations or evaluation challenges. The overall metric tracks closely with English performance, suggesting English evaluation may dominate the composite metric. The consistent gap between English and Chinese performance highlights persistent challenges in Chinese language model development compared to English counterparts.