## Scatter Plot: Model Performance Across Datasets ### Overview The image is a scatter plot comparing the performance of various large language models (LLMs) across different datasets. The y-axis represents the percentage difference (Δ) from a baseline, and the x-axis represents the datasets. Each model is represented by a unique color and marker. ### Components/Axes * **X-axis:** "Dataset" with categories: HotpotQA, CS-QA, AQUA, GSM8K, MATH, GPQA, HumanEval. * **Y-axis:** "Δ (%)" with a numerical scale ranging from -30 to 30, with increments of 10. * **Legend:** Located on the right side of the plot, mapping colors and markers to specific LLMs: * Light Blue Circle: LLaMA3.1-8B * Yellow Circle: LLaMA3.1-70B * Light Purple Circle: Qwen2.5-7B * Red Circle: Qwen2.5-72B * Dark Blue Pentagon: Claude3.5 * Orange Pentagon: GPT-3.5 * Green Pentagon: GPT-4o * Yellow Diamond: QWQ-32B * Dark Purple Diamond: DeepSeek-V3 * White Circle: Open LLM * White Pentagon: Close LLM * White Diamond: Reasoning LLM * Dashed Light Green Line: Baseline (Δ=0) * Vertical dashed lines separate the datasets. ### Detailed Analysis **HotpotQA Dataset:** * LLaMA3.1-8B (Light Blue Circle): Approximately -8% * LLaMA3.1-70B (Yellow Circle): Approximately 8% * Qwen2.5-7B (Light Purple Circle): Approximately 0% * Qwen2.5-72B (Red Circle): Approximately 2% * Claude3.5 (Dark Blue Pentagon): Approximately 5% * GPT-3.5 (Orange Pentagon): Approximately 10% * GPT-4o (Green Pentagon): Approximately 5% * QWQ-32B (Yellow Diamond): Approximately -2% * DeepSeek-V3 (Dark Purple Diamond): Approximately -20% * Open LLM (White Circle): Approximately -10% * Close LLM (White Pentagon): Approximately 0% * Reasoning LLM (White Diamond): Approximately -25% **CS-QA Dataset:** * LLaMA3.1-8B (Light Blue Circle): Approximately -10% * LLaMA3.1-70B (Yellow Circle): Approximately -5% * Qwen2.5-7B (Light Purple Circle): Approximately 0% * Qwen2.5-72B (Red Circle): Approximately -2% * Claude3.5 (Dark Blue Pentagon): Approximately 0% * GPT-3.5 (Orange Pentagon): Approximately 0% * GPT-4o (Green Pentagon): Approximately 0% * QWQ-32B (Yellow Diamond): Approximately 1% * DeepSeek-V3 (Dark Purple Diamond): Approximately -10% * Open LLM (White Circle): Approximately -15% * Close LLM (White Pentagon): Approximately -2% * Reasoning LLM (White Diamond): Approximately 1% **AQUA Dataset:** * LLaMA3.1-8B (Light Blue Circle): Approximately 2% * LLaMA3.1-70B (Yellow Circle): Approximately 8% * Qwen2.5-7B (Light Purple Circle): Approximately 10% * Qwen2.5-72B (Red Circle): Approximately 18% * Claude3.5 (Dark Blue Pentagon): Approximately 15% * GPT-3.5 (Orange Pentagon): Approximately 28% * GPT-4o (Green Pentagon): Approximately 12% * QWQ-32B (Yellow Diamond): Approximately 2% * DeepSeek-V3 (Dark Purple Diamond): Approximately -8% * Open LLM (White Circle): Approximately 1% * Close LLM (White Pentagon): Approximately 10% * Reasoning LLM (White Diamond): Approximately 12% **GSM8K Dataset:** * LLaMA3.1-8B (Light Blue Circle): Approximately 2% * LLaMA3.1-70B (Yellow Circle): Approximately 0% * Qwen2.5-7B (Light Purple Circle): Approximately 0% * Qwen2.5-72B (Red Circle): Approximately 0% * Claude3.5 (Dark Blue Pentagon): Approximately 5% * GPT-3.5 (Orange Pentagon): Approximately 17% * GPT-4o (Green Pentagon): Approximately 2% * QWQ-32B (Yellow Diamond): Approximately 0% * DeepSeek-V3 (Dark Purple Diamond): Approximately 0% * Open LLM (White Circle): Approximately -5% * Close LLM (White Pentagon): Approximately 0% * Reasoning LLM (White Diamond): Approximately -2% **MATH Dataset:** * LLaMA3.1-8B (Light Blue Circle): Approximately 10% * LLaMA3.1-70B (Yellow Circle): Approximately 12% * Qwen2.5-7B (Light Purple Circle): Approximately 10% * Qwen2.5-72B (Red Circle): Approximately 10% * Claude3.5 (Dark Blue Pentagon): Approximately 10% * GPT-3.5 (Orange Pentagon): Approximately 12% * GPT-4o (Green Pentagon): Approximately 10% * QWQ-32B (Yellow Diamond): Approximately 12% * DeepSeek-V3 (Dark Purple Diamond): Approximately -12% * Open LLM (White Circle): Approximately 0% * Close LLM (White Pentagon): Approximately 0% * Reasoning LLM (White Diamond): Approximately 15% **GPQA Dataset:** * LLaMA3.1-8B (Light Blue Circle): Approximately 22% * LLaMA3.1-70B (Yellow Circle): Approximately 25% * Qwen2.5-7B (Light Purple Circle): Approximately 20% * Qwen2.5-72B (Red Circle): Approximately 22% * Claude3.5 (Dark Blue Pentagon): Approximately 5% * GPT-3.5 (Orange Pentagon): Approximately 28% * GPT-4o (Green Pentagon): Approximately 25% * QWQ-32B (Yellow Diamond): Approximately 10% * DeepSeek-V3 (Dark Purple Diamond): Approximately 12% * Open LLM (White Circle): Approximately 2% * Close LLM (White Pentagon): Approximately 2% * Reasoning LLM (White Diamond): Approximately 10% **HumanEval Dataset:** * LLaMA3.1-8B (Light Blue Circle): Approximately -10% * LLaMA3.1-70B (Yellow Circle): Approximately 28% * Qwen2.5-7B (Light Purple Circle): Approximately 10% * Qwen2.5-72B (Red Circle): Approximately 2% * Claude3.5 (Dark Blue Pentagon): Approximately 10% * GPT-3.5 (Orange Pentagon): Approximately 10% * GPT-4o (Green Pentagon): Approximately 10% * QWQ-32B (Yellow Diamond): Approximately 10% * DeepSeek-V3 (Dark Purple Diamond): Approximately 10% * Open LLM (White Circle): Approximately -22% * Close LLM (White Pentagon): Approximately 0% * Reasoning LLM (White Diamond): Approximately 10% ### Key Observations * The performance of the models varies significantly across different datasets. * GPT-3.5 generally performs well, often achieving high percentage differences. * DeepSeek-V3 shows a wide range of performance, with some datasets showing negative percentage differences. * The "Reasoning LLM" (White Diamond) shows a wide range of performance. ### Interpretation The scatter plot provides a comparative analysis of various LLMs across different benchmark datasets. The percentage difference from the baseline indicates how much better or worse each model performs relative to a standard. The variability in performance across datasets suggests that different models are better suited for different types of tasks. For example, GPT-3.5 consistently performs well, indicating a robust general-purpose capability. In contrast, DeepSeek-V3's performance fluctuates, suggesting it may be highly specialized or sensitive to the nature of the task. The plot highlights the importance of evaluating LLMs on a diverse set of benchmarks to understand their strengths and weaknesses. The "Reasoning LLM" shows a wide range of performance, suggesting it may be highly specialized or sensitive to the nature of the task.