## Scatter Plot: Language Model Performance Comparison Across Datasets ### Overview This image is a scatter plot comparing the performance change (Δ%) of various large language models (LLMs) across seven different benchmark datasets. The chart evaluates both open-source and closed-source models, as well as specialized reasoning models, against a baseline performance level. ### Components/Axes * **Chart Type:** Scatter plot with categorical x-axis and numerical y-axis. * **X-Axis (Horizontal):** Labeled "Dataset". It lists seven benchmark categories from left to right: 1. HotpotQA 2. CS-QA 3. AQUA 4. GSM8K 5. MATH 6. GPQA 7. HumanEval * **Y-Axis (Vertical):** Labeled "Δ (%)". It represents the percentage change in performance, with a scale ranging from -30% to +30% in increments of 10%. A horizontal dashed green line at Δ=0 is labeled "Baseline (Δ=0)". * **Legend (Right Side):** The legend is positioned in the top-right quadrant of the chart area. It maps marker colors and shapes to specific models and model categories. * **Models (by color/shape):** * LLaMA3.1-8B (Teal circle) * LLaMA3.1-70B (Light green circle) * Qwen2.5-7B (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) * **Model Categories (by shape):** * Open LLM (Circle) * Close LLM (Pentagon) * Reasoning LLM (Diamond) ### Detailed Analysis The chart is divided into seven vertical sections by dashed grey lines, one for each dataset. Below are the approximate Δ(%) values for each model within each dataset, based on visual estimation of marker position. **1. HotpotQA** * **Trend:** Most models show positive Δ(%) values, indicating performance above the baseline. * **Data Points (Approximate):** * DeepSeek-V3 (Purple diamond): ~+21% (Highest) * LLaMA3.1-8B (Teal circle): ~+13% * Qwen2.5-7B (Purple circle): ~+13% * GPT-3.5 (Orange pentagon): ~+7% * LLaMA3.1-70B (Light green circle): ~+7% * Claude3.5 (Blue pentagon): ~+5% * GPT-4o (Green pentagon): ~+2% * Qwen2.5-72B (Red circle): ~0% * QWQ-32B (Pink diamond): ~-4% **2. CS-QA** * **Trend:** Mixed performance, with several models near or below the baseline. * **Data Points (Approximate):** * Claude3.5 (Blue pentagon): ~+11% (Highest) * GPT-3.5 (Orange pentagon): ~+5% * LLaMA3.1-70B (Light green circle): ~+4% * LLaMA3.1-8B (Teal circle): ~+2% * Qwen2.5-72B (Red circle): ~0% * GPT-4o (Green pentagon): ~-1% * QWQ-32B (Pink diamond): ~-1% * Qwen2.5-7B (Purple circle): ~-7% (Lowest) **3. AQUA** * **Trend:** High variance, with two models showing very high positive Δ(%). * **Data Points (Approximate):** * GPT-3.5 (Orange pentagon): ~+21% (Highest, tied) * QWQ-32B (Pink diamond): ~+21% (Highest, tied) * GPT-4o (Green pentagon): ~+11% * Claude3.5 (Blue pentagon): ~+6% * Qwen2.5-7B (Purple circle): ~+3% * LLaMA3.1-70B (Light green circle): ~0% * Qwen2.5-72B (Red circle): ~0% * DeepSeek-V3 (Purple diamond): ~-6% **4. GSM8K** * **Trend:** Most models cluster near the baseline, with one significant outlier. * **Data Points (Approximate):** * QWQ-32B (Pink diamond): ~+20% (Highest) * GPT-3.5 (Orange pentagon): ~+13% * GPT-4o (Green pentagon): ~+3% * Qwen2.5-72B (Red circle): ~+1% * Claude3.5 (Blue pentagon): ~0% * Qwen2.5-7B (Purple circle): ~-2% * LLaMA3.1-8B (Teal circle): ~-2% * LLaMA3.1-70B (Light green circle): ~-9% (Lowest) **5. MATH** * **Trend:** Generally positive performance, with models clustered in the +5% to +15% range. * **Data Points (Approximate):** * Qwen2.5-72B (Red circle): ~+11% (Highest) * Claude3.5 (Blue pentagon): ~+10% * GPT-4o (Green pentagon): ~+9% * GPT-3.5 (Orange pentagon): ~+8% * LLaMA3.1-70B (Light green circle): ~+6% * LLaMA3.1-8B (Teal circle): ~-6% * QWQ-32B (Pink diamond): ~-1% * DeepSeek-V3 (Purple diamond): ~-2% **6. GPQA** * **Trend:** High variance, with two models showing strong positive Δ(%). * **Data Points (Approximate):** * Claude3.5 (Blue pentagon): ~+21% (Highest) * Qwen2.5-7B (Purple circle): ~+18% * LLaMA3.1-8B (Teal circle): ~+12% * LLaMA3.1-70B (Light green circle): ~+9% * Qwen2.5-72B (Red circle): ~+5% * GPT-3.5 (Orange pentagon): ~+3% * GPT-4o (Green pentagon): ~-3% * QWQ-32B (Pink diamond): ~-4% **7. HumanEval** * **Trend:** Only two data points are visible, both showing negative Δ(%). * **Data Points (Approximate):** * QWQ-32B (Pink diamond): ~+12% * DeepSeek-V3 (Purple diamond): ~-4% ### Key Observations 1. **Model-Specific Strengths:** No single model dominates across all datasets. For example, DeepSeek-V3 excels in HotpotQA (~+21%) but performs poorly in AQUA (~-6%). Claude3.5 shows strong, consistent performance in CS-QA and GPQA. 2. **Dataset Difficulty:** The MATH dataset shows the most consistently positive Δ(%) values across models, suggesting models generally perform better relative to the baseline on this task. In contrast, CS-QA and GSM8K show more models at or below the baseline. 3. **Reasoning Model Performance:** The "Reasoning LLM" category (diamonds) shows extreme variance. QWQ-32B is the top performer in AQUA and GSM8K but is among the lowest in HotpotQA and GPQA. 4. **Open vs. Closed Models:** There is no clear, consistent performance gap between "Open LLM" (circles) and "Close LLM" (pentagons) across all tasks. Their relative performance is highly dataset-dependent. ### Interpretation This chart demonstrates the **task-specific nature of LLM capabilities**. The significant variance in Δ(%) for a given model across different datasets indicates that benchmark performance is not monolithic; a model's architecture, training data, and fine-tuning create specialized strengths and weaknesses. The data suggests that: * **Evaluation is Multidimensional:** Choosing an LLM for a specific application requires benchmarking on tasks relevant to that domain (e.g., a model strong in mathematical reasoning (MATH, GSM8K) may not be the best for complex question answering (HotpotQA, GPQA)). * **The "Best" Model is Contextual:** The absence of a universally superior model implies that the field is still evolving, with different approaches (open vs. closed, general vs. reasoning-specialized) yielding different trade-offs. * **Baseline Matters:** The Δ(%) metric highlights relative improvement or regression against a fixed baseline, which is crucial for understanding progress but doesn't convey absolute performance scores. **Notable Anomaly:** The HumanEval column contains only two data points (QWQ-32B and DeepSeek-V3), while all other datasets have eight or nine. This suggests either missing data for other models on this coding benchmark or a focused comparison for this specific task.