## Scatter Plot: Performance Comparison of Large Language Models
### Overview
This scatter plot compares the performance of several 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. A horizontal dashed line at Δ=0 indicates the baseline performance.
### Components/Axes
* **X-axis:** Dataset - with the following categories: HotpotQA, CS-QA, AQUA, GSM8K, MATH, GPQA, HumanEval.
* **Y-axis:** Δ (%) - Performance Difference in percentage. 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 LLMs included are:
* LLaMA3.1-8B (Light Pink Circle)
* LLaMA3.1-70B (Light Yellow Circle)
* Qwen2.5-7B (Light Blue Circle)
* Qwen2.5-72B (Red Circle)
* Claude3.5 (Dark Blue Triangle)
* GPT-3.5 (Gray Triangle)
* GPT-4o (Purple Diamond)
* QWQ-32B (Orange Diamond)
* DeepSeek-V3 (Dark Purple Diamond)
* Open LLM (White Circle)
* Close LLM (Light Gray Circle)
* Reasoning LLM (Light Blue Diamond)
* Baseline (Δ=0) (Horizontal Dashed Green Line)
### Detailed Analysis
The plot shows the performance difference of each LLM relative to a baseline (Δ=0) on each dataset.
* **HotpotQA:** Most models cluster around 0% to 10%. LLaMA3.1-8B shows a slight negative difference (around -5%), while Qwen2.5-72B shows a positive difference (around 5-10%).
* **CS-QA:** Similar to HotpotQA, most models are within 0% to 10%. Qwen2.5-72B shows a more pronounced positive difference (around 10-15%).
* **AQUA:** A wider range of performance differences is observed. GPT-4o and DeepSeek-V3 show the highest positive differences (around 15-25%). LLaMA3.1-8B and Qwen2.5-7B show negative differences (around -5% to -10%).
* **GSM8K:** GPT-4o and DeepSeek-V3 again exhibit the largest positive differences (around 15-25%). Qwen2.5-72B shows a moderate positive difference (around 5-10%).
* **MATH:** GPT-4o and DeepSeek-V3 have the highest positive differences (around 20-30%). Other models are generally closer to the baseline.
* **GPQA:** GPT-4o and DeepSeek-V3 show significant positive differences (around 15-25%). Claude3.5 also shows a positive difference (around 10%).
* **HumanEval:** GPT-4o shows a large positive difference (around 15-20%). Qwen2.5-72B shows a moderate positive difference (around 5-10%).
**Specific Data Points (Approximate):**
* **GPT-4o:** AQUA (~22%), GSM8K (~22%), MATH (~28%), GPQA (~20%), HumanEval (~18%)
* **DeepSeek-V3:** AQUA (~18%), GSM8K (~18%), MATH (~20%), GPQA (~15%)
* **Qwen2.5-72B:** HotpotQA (~8%), CS-QA (~12%), AQUA (~5%), GSM8K (~8%), MATH (~2%), GPQA (~5%), HumanEval (~8%)
* **LLaMA3.1-8B:** HotpotQA (~-5%), CS-QA (~2%), AQUA (~-8%), GSM8K (~-2%), MATH (~-2%), GPQA (~2%), HumanEval (~2%)
### Key Observations
* GPT-4o and DeepSeek-V3 consistently outperform other models across most datasets, particularly on the more challenging ones (AQUA, GSM8K, MATH, GPQA).
* Qwen2.5-72B generally performs better than Qwen2.5-7B.
* LLaMA3.1-8B shows relatively lower performance compared to other models, especially on AQUA and GSM8K.
* The performance differences are more pronounced on datasets requiring reasoning and mathematical abilities (AQUA, GSM8K, MATH, GPQA).
### Interpretation
The data suggests that GPT-4o and DeepSeek-V3 are currently the leading LLMs in terms of overall performance, demonstrating superior capabilities in complex reasoning and mathematical problem-solving. The consistent outperformance of these models across multiple datasets indicates a robust and generalizable advantage. The performance differences between the 7B and 72B versions of Qwen highlight the importance of model size for achieving higher accuracy. The relatively lower performance of LLaMA3.1-8B suggests that it may require further optimization or scaling to compete with the state-of-the-art models. The datasets themselves appear to be ordered by difficulty, with the more challenging datasets exhibiting larger performance variations between models. The baseline (Δ=0) serves as a crucial reference point, allowing for a clear assessment of the relative improvements or declines in performance achieved by each LLM.
