## Line Chart: Accuracy Comparison of AI Models on Chinese Tasks ### Overview The chart compares the accuracy performance of four AI models (InternLM2-20B, Yi-34B, Qwen-72B, GPT-3.5) across 30+ Chinese language tasks. Accuracy is measured on a 0-100% scale, with significant fluctuations observed across different tasks. The green line (Qwen-72B) generally maintains the highest median accuracy, while the blue line (InternLM2-20B) shows the most volatility. ### Components/Axes - **X-axis**: Chinese task categories (30+ labels in Chinese, e.g., "全球化" [Globalization], "人工智能" [Artificial Intelligence], "环保" [Environmental Protection]) - **Y-axis**: Accuracy percentage (0-100 scale) - **Legend**: - Blue: InternLM2-20B - Orange: Yi-34B - Green: Qwen-72B - Red: GPT-3.5 - **Positioning**: Legend at top-center; X-axis labels at bottom (horizontal orientation) ### Detailed Analysis 1. **Qwen-72B (Green)**: - Peaks at 100% for "全球化" and "人工智能" - Maintains >80% accuracy for 12+ tasks - Lowest point: ~35% for "环保" 2. **GPT-3.5 (Red)**: - Peaks at ~85% for "人工智能" and "经济" (Economics) - Drops below 40% for "环保" and "教育" (Education) - Shows bimodal distribution with two distinct performance clusters 3. **Yi-34B (Orange)**: - Peaks at ~75% for "科技" (Technology) - Most consistent performer among smaller models - Average accuracy: ~55% 4. **InternLM2-20B (Blue)**: - Highest volatility (range: 10-90%) - Peaks at ~90% for "科技" - Lowest point: ~10% for "环保" ### Key Observations - **Performance Gaps**: Qwen-72B outperforms others by 20-30% on average across all tasks - **Task Sensitivity**: All models show >50% variance between their best and worst tasks - **Model Size Correlation**: Larger models (Qwen-72B) demonstrate more consistent high performance - **Anomalies**: - InternLM2-20B's 10% "环保" accuracy is 80% below its peak - GPT-3.5's 85% "人工智能" accuracy matches Qwen-72B's performance ### Interpretation The data suggests model architecture and training data specialization significantly impact task performance. Qwen-72B's consistent high accuracy indicates superior handling of complex Chinese language nuances. GPT-3.5's bimodal distribution suggests either specialized training or data biases in specific domains. The extreme volatility of InternLM2-20B highlights challenges in smaller models maintaining performance across diverse tasks. The 100% accuracy peaks for Qwen-72B on "全球化" and "人工智能" may indicate overfitting to these specific task patterns. **Note**: Chinese task labels have been transcribed with pinyin and English translations for reference. All accuracy values are approximate due to visual estimation limitations.