\n ## Scatter Plot: Performance Comparison of Large Language Models ### Overview This image presents a scatter plot comparing the performance of several Large Language Models (LLMs) across seven different datasets. The y-axis represents the percentage difference (Δ (%)) in performance relative to a baseline, and the x-axis lists the datasets used for evaluation. The plot uses different colors and markers to distinguish between the various LLMs. Vertical dashed lines separate each dataset. ### Components/Axes * **X-axis:** Dataset - labeled with the following datasets: HotpotQA, CS-QA, AQUA, GSM8K, MATH, GPQA, and HumanEval. * **Y-axis:** Δ (%) - Represents the percentage difference in performance compared to a baseline (Δ=0). The scale ranges from approximately -30% to 30%. * **Legend (Top-Right):** Contains labels and corresponding colors/markers for each LLM: * LLaMA3.1-8B (Light Blue Circle) * LLaMA3.1-70B (Light Green Circle) * Qwen.2.5-7B (Light Purple Circle) * Qwen.2.5-72B (Red Circle) * Claude3.5 (Dark Blue Triangle) * GPT-3.5 (Orange Triangle) * GPT-4.0 (Dark Green Triangle) * QWQ-32B (Dark Purple Diamond) * DeepSeek-V3 (Pink Diamond) * Open LLM (Black Hexagon) * Close LLM (Gray Hexagon) * Reasoning LLM (White Diamond) * Baseline (Δ=0) (Horizontal Dashed Red Line) ### Detailed Analysis The data points are scattered across the plot, indicating varying performance levels for each LLM on each dataset. The baseline is represented by a horizontal dashed red line at Δ=0. * **HotpotQA:** * LLaMA3.1-8B: Approximately +5% to +15% * LLaMA3.1-70B: Approximately +5% to +10% * Qwen.2.5-7B: Approximately -5% to +10% * Qwen.2.5-72B: Approximately -5% to +5% * Claude3.5: Approximately -10% to +5% * GPT-3.5: Approximately +5% to +15% * GPT-4.0: Approximately +5% to +10% * QWQ-32B: Approximately -5% to +5% * DeepSeek-V3: Approximately -10% to +10% * Open LLM: Approximately -10% to +5% * Close LLM: Approximately -10% to +5% * Reasoning LLM: Approximately -10% to +5% * **CS-QA:** * LLaMA3.1-8B: Approximately -10% to +10% * LLaMA3.1-70B: Approximately -10% to +10% * Qwen.2.5-7B: Approximately -10% to +10% * Qwen.2.5-72B: Approximately -10% to +10% * Claude3.5: Approximately -10% to +5% * GPT-3.5: Approximately -10% to +5% * GPT-4.0: Approximately -10% to +5% * QWQ-32B: Approximately -10% to +5% * DeepSeek-V3: Approximately -10% to +5% * Open LLM: Approximately -10% to +5% * Close LLM: Approximately -10% to +5% * Reasoning LLM: Approximately -10% to +5% * **AQUA:** * LLaMA3.1-8B: Approximately -10% to +10% * LLaMA3.1-70B: Approximately -10% to +10% * Qwen.2.5-7B: Approximately -10% to +10% * Qwen.2.5-72B: Approximately -10% to +10% * Claude3.5: Approximately -10% to +5% * GPT-3.5: Approximately -10% to +5% * GPT-4.0: Approximately -10% to +5% * QWQ-32B: Approximately -10% to +5% * DeepSeek-V3: Approximately -10% to +5% * Open LLM: Approximately -10% to +5% * Close LLM: Approximately -10% to +5% * Reasoning LLM: Approximately -10% to +5% * **GSM8K:** * LLaMA3.1-8B: Approximately -10% to +20% * LLaMA3.1-70B: Approximately -10% to +10% * Qwen.2.5-7B: Approximately -10% to +10% * Qwen.2.5-72B: Approximately -10% to +10% * Claude3.5: Approximately -10% to +5% * GPT-3.5: Approximately -10% to +5% * GPT-4.0: Approximately -10% to +5% * QWQ-32B: Approximately -10% to +5% * DeepSeek-V3: Approximately -10% to +5% * Open LLM: Approximately -10% to +5% * Close LLM: Approximately -10% to +5% * Reasoning LLM: Approximately -10% to +5% * **MATH:** * LLaMA3.1-8B: Approximately -20% to +10% * LLaMA3.1-70B: Approximately -20% to +10% * Qwen.2.5-7B: Approximately -20% to +10% * Qwen.2.5-72B: Approximately -20% to +10% * Claude3.5: Approximately -20% to +5% * GPT-3.5: Approximately -20% to +5% * GPT-4.0: Approximately -20% to +5% * QWQ-32B: Approximately -20% to +5% * DeepSeek-V3: Approximately -20% to +5% * Open LLM: Approximately -20% to +5% * Close LLM: Approximately -20% to +5% * Reasoning LLM: Approximately -20% to +5% * **GPQA:** * LLaMA3.1-8B: Approximately -10% to +10% * LLaMA3.1-70B: Approximately -10% to +10% * Qwen.2.5-7B: Approximately -10% to +10% * Qwen.2.5-72B: Approximately -10% to +10% * Claude3.5: Approximately -10% to +5% * GPT-3.5: Approximately -10% to +5% * GPT-4.0: Approximately -10% to +5% * QWQ-32B: Approximately -10% to +5% * DeepSeek-V3: Approximately -10% to +5% * Open LLM: Approximately -10% to +5% * Close LLM: Approximately -10% to +5% * Reasoning LLM: Approximately -10% to +5% * **HumanEval:** * LLaMA3.1-8B: Approximately -20% to +5% * LLaMA3.1-70B: Approximately -20% to +5% * Qwen.2.5-7B: Approximately -20% to +5% * Qwen.2.5-72B: Approximately -20% to +5% * Claude3.5: Approximately -20% to +5% * GPT-3.5: Approximately -20% to +5% * GPT-4.0: Approximately -20% to +5% * QWQ-32B: Approximately -20% to +5% * DeepSeek-V3: Approximately -20% to +5% * Open LLM: Approximately -20% to +5% * Close LLM: Approximately -20% to +5% * Reasoning LLM: Approximately -20% to +5% ### Key Observations * The performance varies significantly across datasets. * LLaMA3.1-8B and LLaMA3.1-70B generally perform similarly. * GPT-4.0 consistently shows strong performance across most datasets. * Some models (e.g., DeepSeek-V3, QWQ-32B) exhibit more variability in performance across datasets. * The "Reasoning LLM" consistently performs near or below the baseline. ### Interpretation This plot provides a comparative analysis of LLM performance on a range of tasks represented by the different datasets. The Δ (%) metric allows for a direct comparison of how much better or worse each model performs relative to a baseline. The variability in performance across datasets suggests that different LLMs excel at different types of tasks. For example, a model that performs well on HotpotQA (question answering) might not perform as well on MATH (mathematical reasoning). The consistent strong performance of GPT-4.0 indicates its robustness and generalizability. The relatively poor performance of the "Reasoning LLM" suggests that reasoning capabilities are a challenge for this particular model or approach. The data suggests that model size (e.g., comparing LLaMA3.1-8B and LLaMA3.1-70B) does not always translate to significantly better performance, indicating that other factors, such as training data and model architecture, also play a crucial role.