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## Scatter Plot: LLM Performance Delta Across Datasets
### Overview
The image is a scatter plot comparing the performance change (Δ, in percentage) of various Large Language Models (LLMs) across seven different benchmark datasets. Each data point represents a specific model's performance delta relative to a baseline (Δ=0). The models are categorized as "Open LLM," "Close LLM," or "Reasoning LLM" using distinct marker shapes.
### Components/Axes
* **Chart Type:** Scatter plot with categorical x-axis.
* **X-Axis (Horizontal):** Labeled "Dataset." It lists seven benchmark categories, separated by vertical dashed lines. 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. The scale runs from -30 to 30, with major tick marks at intervals of 10 (-30, -20, -10, 0, 10, 20, 30).
* **Baseline:** A horizontal, light green dashed line at y=0, labeled "Baseline (Δ=0)" in the legend.
* **Legend (Right Side):** Positioned to the right of the plot area. It maps model names to specific marker colors and shapes, and defines the marker categories.
* **Models & Colors:**
* LLaMA3.1-8B: Teal circle
* LLaMA3.1-70B: Light yellow circle
* Qwen2.5-7B: Light purple circle
* Qwen2.5-72B: Salmon/red circle
* Claude3.5: Blue pentagon
* GPT-3.5: Orange pentagon
* GPT-4o: Green pentagon
* QWQ-32B: Purple diamond
* DeepSeek-V3: Pink diamond
* **Marker Categories:**
* Open LLM: Circle (○)
* Close LLM: Pentagon (⬠)
* Reasoning LLM: Diamond (◇)
### Detailed Analysis
Data points are grouped vertically above each dataset label. Values are approximate, estimated from the y-axis position.
**1. HotpotQA**
* **Above Baseline (Δ > 0):** LLaMA3.1-8B (~1%), LLaMA3.1-70B (~1%), Qwen2.5-72B (~1%), GPT-4o (~2%), QWQ-32B (~4%).
* **Below Baseline (Δ < 0):** Qwen2.5-7B (~-2%), Claude3.5 (~-7%), GPT-3.5 (~-7%), DeepSeek-V3 (~-4%).
**2. CS-QA**
* **Above Baseline:** Qwen2.5-7B (~10%), Qwen2.5-72B (~2%), Claude3.5 (~-3% *Note: This point is slightly below 0*), GPT-3.5 (~-1%), GPT-4o (~0%), QWQ-32B (~3%).
* **Below Baseline:** LLaMA3.1-8B (~0%), LLaMA3.1-70B (~-1%), DeepSeek-V3 (~-6%).
**3. AQUA**
* **Above Baseline:** LLaMA3.1-70B (~5%), Qwen2.5-7B (~18%), Qwen2.5-72B (~7%), Claude3.5 (~9%), GPT-3.5 (~10%), GPT-4o (~12%).
* **Below Baseline:** LLaMA3.1-8B (~1%), QWQ-32B (~-1%), DeepSeek-V3 (~3% *Note: This point is slightly above 0*).
**4. GSM8K**
* **Above Baseline:** Qwen2.5-7B (~5%), Qwen2.5-72B (~0%), Claude3.5 (~9%), GPT-3.5 (~8%), GPT-4o (~10%).
* **Below Baseline:** LLaMA3.1-8B (~-9%), LLaMA3.1-70B (~-4%), QWQ-32B (~-3%), DeepSeek-V3 (~-1%).
**5. MATH**
* **Above Baseline:** LLaMA3.1-70B (~8%), Qwen2.5-7B (~11%), Qwen2.5-72B (~14%), Claude3.5 (~4%), GPT-3.5 (~4%), GPT-4o (~4%).
* **Below Baseline:** LLaMA3.1-8B (~-3%), QWQ-32B (~-8%), DeepSeek-V3 (~-1%).
**6. GPQA**
* **Above Baseline:** LLaMA3.1-70B (~11%), Qwen2.5-7B (~16%), Qwen2.5-72B (~6%), Claude3.5 (~5%), GPT-3.5 (~11%), GPT-4o (~3%), QWQ-32B (~5%), DeepSeek-V3 (~5%).
* **Below Baseline:** LLaMA3.1-8B (~4% *Note: This point is above 0*).
**7. HumanEval**
* **Above Baseline:** LLaMA3.1-70B (~4%), Qwen2.5-7B (~-1% *Note: This point is slightly below 0*), Claude3.5 (~0%), GPT-3.5 (~-2%), GPT-4o (~9%), QWQ-32B (~3%), DeepSeek-V3 (~2%).
* **Below Baseline:** LLaMA3.1-8B (~-11%), Qwen2.5-72B (~-1%).
### Key Observations
1. **High Variance in AQUA & GPQA:** The AQUA and GPQA datasets show the widest spread of performance deltas, with several models achieving gains above +10% and others falling below the baseline.
2. **Consistently Strong on MATH:** Most models show a positive performance delta on the MATH dataset, with Qwen2.5-72B showing the highest gain (~+14%).
3. **LLaMA3.1-8B Struggles:** The LLaMA3.1-8B model (teal circle) frequently appears below the baseline, most notably on GSM8K (~-9%) and HumanEval (~-11%).
4. **Qwen2.5-7B's Peak:** The Qwen2.5-7B model (light purple circle) achieves the single highest observed delta on the chart, at approximately +18% on the AQUA dataset.
5. **Reasoning LLMs (Diamonds):** The "Reasoning LLM" category (QWQ-32B, DeepSeek-V3) shows mixed results. QWQ-32B has a notable positive spike on HotpotQA, while DeepSeek-V3 is often near or slightly below the baseline, except for a positive showing on GPQA.
6. **Close LLMs (Pentagons):** The "Close LLM" models (Claude3.5, GPT-3.5, GPT-4o) generally cluster together within each dataset, often showing positive deltas, particularly on AQUA and MATH.
### Interpretation
This chart visualizes a comparative benchmark study, likely measuring the improvement (or degradation) of various LLMs when using a specific technique, prompting method, or model variant compared to a standard baseline. The "Δ (%)" suggests a relative performance metric.
* **Dataset Sensitivity:** Model performance is highly dataset-dependent. A model excelling in one domain (e.g., Qwen2.5-7B on AQUA) may not lead in another (e.g., HumanEval). This underscores the importance of multi-faceted evaluation.
* **Model Size vs. Performance:** Larger models (e.g., LLaMA3.1-70B, Qwen2.5-72B) do not universally outperform smaller ones (e.g., Qwen2.5-7B) across all tasks, indicating that architecture, training data, or task alignment play critical roles.
* **Specialization:** The strong performance of several models on the MATH dataset suggests the evaluated technique or the models themselves are particularly effective for mathematical reasoning tasks. Conversely, the mixed results on code (HumanEval) and complex reasoning (HotpotQA, GPQA) indicate these remain challenging areas.
* **The "Reasoning LLM" Category:** The inclusion of a specific "Reasoning LLM" category implies these models (QWQ-32B, DeepSeek-V3) may have been designed or fine-tuned with a focus on logical inference. Their variable performance suggests that "reasoning" capability is not monolithic and manifests differently across benchmark types.
**Language Note:** All text in the image is in English.
