## 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.

\n ## Scatter Plot: Performance Comparison of Large Language Models ### Overview This scatter plot compares the performance of various Large Language Models (LLMs) across seven different datasets. The y-axis represents the performance difference (Δ) in percentage points, while the x-axis lists the datasets used for evaluation. The plot uses different marker shapes and colors to distinguish between the LLMs. A horizontal dashed line at Δ=0 serves as a baseline for comparison. ### Components/Axes * **X-axis:** Dataset - with the following categories: HotpotQA, CS-QA, AQUA, GSM8K, MATH, GPQA, HumanEval. * **Y-axis:** Δ (%) - Represents the percentage point difference in performance relative to a baseline. The scale ranges from approximately -30% to 30%. * **Legend:** Located in the top-right corner, identifies each LLM with a corresponding color and marker shape. The legend includes: * LLaMA3.1-8B (Purple Circle) * LLaMA3.1-70B (Yellow Circle) * Qwen2.5-7B (Light Blue Circle) * Qwen2.5-72B (Red Circle) * Claude3.5 (Dark Blue Triangle) * GPT-3.5 (Orange Triangle) * GPT-4o (Green Diamond) * QWQ-32B (Brown Diamond) * DeepSeek-V3 (Magenta Diamond) * Open LLM (White Circle) * Close LLM (Light Grey Hexagon) * Reasoning LLM (Black Diamond) * Baseline (Δ=0) (Black Dashed Line) ### Detailed Analysis The plot shows the performance variation of each LLM across the datasets. The following observations are made: * **HotpotQA:** Performance varies significantly. LLaMA3.1-70B shows the highest positive Δ (approximately +20%), while Qwen2.5-72B shows a negative Δ (approximately -10%). * **CS-QA:** LLaMA3.1-70B and Qwen2.5-72B show positive Δ values (around +10% to +15%), while other models are closer to the baseline. * **AQUA:** GPT-4o and DeepSeek-V3 show the highest positive Δ values (around +20% to +25%). Qwen2.5-7B and Qwen2.5-72B show negative Δ values (around -5% to -10%). * **GSM8K:** GPT-4o and DeepSeek-V3 show the highest positive Δ values (around +20% to +25%). Qwen2.5-72B shows a negative Δ value (around -15%). * **MATH:** GPT-4o and DeepSeek-V3 show the highest positive Δ values (around +20% to +30%). Qwen2.5-72B shows a negative Δ value (around -20%). * **GPQA:** LLaMA3.1-70B and DeepSeek-V3 show the highest positive Δ values (around +15% to +25%). Qwen2.5-72B shows a negative Δ value (around -10%). * **HumanEval:** GPT-4o shows the highest positive Δ value (around +20%). Qwen2.5-72B shows a negative Δ value (around -20%). **Specific Data Points (Approximate):** * **LLaMA3.1-8B:** Δ values range from approximately -5% to +15% across datasets. * **LLaMA3.1-70B:** Δ values range from approximately -5% to +20% across datasets. * **Qwen2.5-7B:** Δ values range from approximately -10% to +10% across datasets. * **Qwen2.5-72B:** Δ values range from approximately -20% to +15% across datasets. * **Claude3.5:** Δ values range from approximately -5% to +10% across datasets. * **GPT-3.5:** Δ values range from approximately -5% to +10% across datasets. * **GPT-4o:** Δ values range from approximately +10% to +30% across datasets. * **QWQ-32B:** Δ values range from approximately -5% to +15% across datasets. * **DeepSeek-V3:** Δ values range from approximately +10% to +25% across datasets. ### Key Observations * GPT-4o and DeepSeek-V3 consistently outperform other models across most datasets, exhibiting the largest positive Δ values. * Qwen2.5-72B frequently shows negative Δ values, indicating underperformance compared to the baseline. * LLaMA3.1-70B generally performs better than LLaMA3.1-8B. * The performance differences between models are dataset-dependent. ### Interpretation The data suggests that GPT-4o and DeepSeek-V3 are currently the most capable LLMs among those tested, demonstrating superior performance across a diverse range of tasks represented by the datasets. The consistent negative Δ values for Qwen2.5-72B indicate that this model may require further optimization or is less suited for the evaluated tasks. The variation in performance across datasets highlights the importance of task-specific evaluation when comparing LLMs. The plot provides a valuable comparative analysis of LLM capabilities, enabling informed decisions regarding model selection for specific applications. The baseline (Δ=0) is crucial for understanding whether a model is providing an improvement over a standard approach. The distinction between "Open LLM", "Close LLM", and "Reasoning LLM" suggests a categorization of models based on their architecture or training methodology, but the plot doesn't directly reveal how these categories correlate with performance.

## Scatter Plot: Performance Delta of Various Language Models Across Datasets ### Overview The image is a scatter plot comparing the performance change (Δ%) of multiple large language models (LLMs) across seven different benchmark datasets. Each data point represents a specific model's performance on a dataset relative to a baseline (Δ=0). The models are categorized as Open LLMs, Closed LLMs, or Reasoning LLMs, indicated by different marker shapes. ### Components/Axes * **Chart Type:** Scatter plot with categorical x-axis. * **X-Axis (Horizontal):** Labeled "Dataset". Categories from left to right: `HotpotQA`, `CS-QA`, `AQUA`, `GSM8K`, `MATH`, `GPQA`, `HumanEval`. * **Y-Axis (Vertical):** Labeled "Δ (%)". Scale ranges from -30 to 30, with major tick marks at intervals of 10 (-30, -20, -10, 0, 10, 20, 30). * **Baseline:** A horizontal dashed green line at `Δ=0`, labeled "Baseline (Δ=0)" in the legend. * **Legend (Right Side):** Lists models and marker types. * **Models (by color):** * `LLaMA3.1-8B` (Teal circle) * `LLaMA3.1-70B` (Light green circle) * `Qwen2.5-7B` (Light purple circle) * `Qwen2.5-72B` (Red circle) * `Claude3.5` (Blue pentagon) * `GPT-3.5` (Orange pentagon) * `GPT-4o` (Green pentagon) * `QWQ-32B` (Pink diamond) * `DeepSeek-V3` (Purple diamond) * **Marker Types (by shape):** * `Open LLM` (Circle) * `Close LLM` (Pentagon) * `Reasoning LLM` (Diamond) ### Detailed Analysis The chart shows the performance delta (Δ%) for each model on each dataset. Below is an approximate extraction of data points, grouped by dataset. Values are estimated from the plot's grid. **1. HotpotQA:** * LLaMA3.1-8B: ~ -8% * LLaMA3.1-70B: ~ +8% * Qwen2.5-7B: ~ -1% * Qwen2.5-72B: ~ +6% * Claude3.5: ~ +1% * GPT-3.5: ~ +3% * GPT-4o: ~ +2% * QWQ-32B: ~ +14% * DeepSeek-V3: ~ -1% **2. CS-QA:** * LLaMA3.1-8B: ~ -10% * LLaMA3.1-70B: ~ -5% * Qwen2.5-7B: ~ -16% * Qwen2.5-72B: ~ -1% * Claude3.5: ~ +10% * GPT-3.5: ~ +12% * GPT-4o: ~ +8% * QWQ-32B: ~ -10% * DeepSeek-V3: ~ 0% **3. AQUA:** * LLaMA3.1-8B: ~ -3% * LLaMA3.1-70B: ~ +14% * Qwen2.5-7B: ~ +10% * Qwen2.5-72B: ~ +18% * Claude3.5: ~ +2% * GPT-3.5: ~ +26% * GPT-4o: ~ +10% * QWQ-32B: ~ +7% * DeepSeek-V3: ~ +6% **4. GSM8K:** * LLaMA3.1-8B: ~ -1% * LLaMA3.1-70B: ~ -4% * Qwen2.5-7B: ~ +1% * Qwen2.5-72B: ~ +3% * Claude3.5: ~ +2% * GPT-3.5: ~ +16% * GPT-4o: ~ +5% * QWQ-32B: ~ +30% (Highest point on chart) * DeepSeek-V3: ~ 0% **5. MATH:** * LLaMA3.1-8B: ~ 0% * LLaMA3.1-70B: ~ +9% * Qwen2.5-7B: ~ +11% * Qwen2.5-72B: ~ -3% * Claude3.5: ~ +2% * GPT-3.5: ~ +14% * GPT-4o: ~ +16% * QWQ-32B: ~ -7% * DeepSeek-V3: ~ -12% **6. GPQA:** * LLaMA3.1-8B: ~ +6% * LLaMA3.1-70B: ~ +24% * Qwen2.5-7B: ~ +29% (Highest for this dataset) * Qwen2.5-72B: ~ +23% * Claude3.5: ~ +11% * GPT-3.5: ~ +21% * GPT-4o: ~ +23% * QWQ-32B: ~ +14% * DeepSeek-V3: ~ +21% **7. HumanEval:** * LLaMA3.1-8B: ~ -10% * LLaMA3.1-70B: ~ +25% * Qwen2.5-7B: ~ +6% * Qwen2.5-72B: ~ -23% (Lowest point on chart) * Claude3.5: ~ 0% * GPT-3.5: ~ +4% * GPT-4o: ~ +9% * QWQ-32B: ~ -9% * DeepSeek-V3: ~ -9% ### Key Observations 1. **High Variance in HumanEval:** The `HumanEval` dataset shows the widest spread of performance deltas, ranging from approximately -23% (Qwen2.5-72B) to +25% (LLaMA3.1-70B). 2. **Top Performer on GSM8K:** The reasoning model `QWQ-32B` achieves the single highest performance delta on the chart, at approximately +30% on the `GSM8K` dataset. 3. **Consistently Strong Closed LLMs:** Closed models like `GPT-4o` and `Claude3.5` show generally positive deltas across most datasets, with `GPT-4o` never dropping below the baseline. 4. **Model-Specific Strengths/Weaknesses:** * `LLaMA3.1-70B` performs strongly on `GPQA` and `HumanEval` but negatively on `CS-QA`. * `Qwen2.5-72B` shows a significant negative outlier on `HumanEval` but strong positive results on `AQUA` and `GPQA`. * `DeepSeek-V3` (Reasoning LLM) shows mixed results, with its best performance on `GPQA` and its worst on `MATH`. 5. **Baseline Comparison:** The majority of data points lie above the `Δ=0` baseline, suggesting that most models, on most datasets, show a performance change (likely improvement, though the direction of "delta" isn't explicitly defined as improvement) relative to the baseline. ### Interpretation This chart provides a comparative snapshot of LLM performance across diverse reasoning and knowledge tasks. The data suggests that: * **Task-Specific Performance:** No single model dominates across all datasets. Performance is highly contingent on the specific benchmark, indicating that model capabilities are specialized. For example, a model strong in mathematical reasoning (`GSM8K`, `MATH`) may not be the best at code generation (`HumanEval`) or complex question answering (`HotpotQA`). * **Scale vs. Architecture:** Larger open models (e.g., `LLaMA3.1-70B`) often outperform their smaller counterparts, but not universally. The presence of reasoning-specialized models (`QWQ-32B`, `DeepSeek-V3`) with high variance suggests that architectural focus can lead to exceptional performance in specific domains (like math for QWQ-32B) but may not generalize. * **The "Delta" Ambiguity:** The chart uses "Δ (%)" without specifying the reference point or direction. Assuming Δ=0 is a previous model version or a standard baseline, positive values indicate improvement. The widespread positive deltas could reflect recent advancements in the field. The significant negative outlier for `Qwen2.5-72B` on `HumanEval` warrants investigation—it could indicate a regression, a benchmarking anomaly, or a specific weakness in code generation for that model version. * **Benchmark Diversity:** The selection of datasets covers a broad spectrum: multi-hop reasoning (HotpotQA), science QA (CS-QA, GPQA), math (AQUA, GSM8K, MATH), and coding (HumanEval). This diversity is crucial for a holistic evaluation, as the chart clearly shows that model rankings shift dramatically depending on the task. In essence, the visualization underscores the complexity of evaluating LLMs, highlighting that "best" is a context-dependent label and that the field continues to exhibit rapid, uneven progress across different cognitive domains.

## Scatter Plot: Language Model Performance Comparison Across Datasets ### Overview The image is a scatter plot comparing the performance of various large language models (LLMs) across seven datasets. The y-axis represents percentage change (Δ%) relative to a baseline (Δ=0), while the x-axis lists datasets. Each model is represented by a unique color and marker, with performance variations visualized as data points. ### Components/Axes - **X-Axis (Dataset)**: - Categories: HotpotQA, CS-QA, AQUA, GSM8K, MATH, GPQA, HumanEval - Separated by vertical dashed lines. - **Y-Axis (Δ%)**: - Range: -30% to 30%, with a green dashed baseline at 0%. - **Legend**: - Located on the right, mapping 10 models to colors/shapes: - LLaMA3.1-8B (teal circles) - LLaMA3.1-70B (yellow circles) - Qwen2.5-7B (purple circles) - Qwen2.5-72B (red circles) - Claude3.5 (blue pentagons) - GPT-3.5 (orange pentagons) - GPT-4o (green pentagons) - OWO-32B (pink diamonds) - DeepSeek-V3 (purple diamonds) - Open LLM (open circles) - Close LLM (open pentagons) - Reasoning LLM (open diamonds) ### Detailed Analysis - **Dataset-Specific Trends**: - **HotpotQA**: - LLaMA3.1-8B (-10%), Qwen2.5-7B (-15%), GPT-4o (+5%). - **CS-QA**: - GPT-3.5 (+12%), OWO-32B (+8%), DeepSeek-V3 (+3%). - **AQUA**: - LLaMA3.1-70B (+15%), Claude3.5 (+2%), Qwen2.5-72B (-5%). - **GSM8K**: - GPT-4o (+10%), Qwen2.5-7B (+7%), Reasoning LLM (+1%). - **MATH**: - GPT-4o (+15%), DeepSeek-V3 (+20%), LLaMA3.1-8B (-2%). - **GPQA**: - Qwen2.5-72B (+22%), OWO-32B (+18%), LLaMA3.1-70B (+12%). - **HumanEval**: - Qwen2.5-72B (-20%), GPT-4o (+9%), DeepSeek-V3 (-10%). - **Model Performance**: - **Highest Gains**: - DeepSeek-V3 (+20% on MATH), Qwen2.5-72B (+22% on GPQA). - **Largest Declines**: - Qwen2.5-72B (-20% on HumanEval), LLaMA3.1-8B (-10% on HotpotQA). - **Consistent Performance**: - GPT-4o shows positive Δ% across all datasets (range: +5% to +15%). ### Key Observations 1. **Outliers**: - Qwen2.5-72B exhibits extreme variability (e.g., +22% on GPQA vs. -20% on HumanEval). - DeepSeek-V3 has the highest gain (+20% on MATH) but also a notable drop (-10% on HumanEval). 2. **Baseline Deviations**: - 60% of data points fall above the baseline (Δ>0), indicating most models outperform the baseline on at least one dataset. - 30% of points fall below the baseline (Δ<0), highlighting underperformance in specific cases. 3. **Model Specialization**: - GPT-4o and DeepSeek-V3 dominate in reasoning-heavy datasets (MATH, GPQA). - Qwen2.5-72B excels in GPQA but struggles with HumanEval. ### Interpretation The data suggests that model performance is highly dataset-dependent. GPT-4o and DeepSeek-V3 demonstrate robustness across reasoning tasks, while Qwen2.5-72B shows dataset-specific strengths and weaknesses. The baseline (Δ=0) serves as a critical reference, revealing that even top models underperform in certain contexts (e.g., HumanEval for Qwen2.5-72B). The variability underscores the need for dataset-specific optimization in LLM deployment. Notably, Open LLM and Close LLM categories lack distinct performance patterns, suggesting potential overlap in their evaluation metrics.