## Scatter Plot: Performance Change (Δ%) of Various LLMs Across Multiple Datasets
### Overview
The image is a scatter plot comparing the performance change (Δ%) of multiple Large Language Models (LLMs) across seven different benchmark datasets. The plot uses distinct symbols and colors to represent each model, with a horizontal baseline at Δ=0 indicating no change in performance. The data points are grouped by dataset along the x-axis.
### Components/Axes
* **X-axis (Categorical):** Labeled "Dataset". The seven datasets listed from left to right are:
1. HotpotQA
2. CS-QA
3. AQUA
4. GSM8K
5. MATH
6. GPQA
7. HumanEval
* **Y-axis (Numerical):** Labeled "Δ(%)". The scale ranges from -30 to +30, with major tick marks at intervals of 10 (-30, -20, -10, 0, 10, 20, 30).
* **Legend (Top-Right):** Contains 12 entries, each pairing a model name with a unique symbol/color:
* LLaMA3.1-8B: Light blue circle
* LLaMA3.1-70B: Yellow 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: Purple diamond
* DeepSeek-V3: Pink diamond
* Open LLM: Circle symbol (category)
* Close LLM: Pentagon symbol (category)
* Reasoning LLM: Diamond symbol (category)
* **Baseline:** A horizontal, dashed, light green line at y=0, labeled "Baseline (Δ=0)".
### Detailed Analysis
Performance change (Δ%) is plotted for each model on each dataset. Values are approximate based on visual positioning.
**1. HotpotQA:**
* All models show negative Δ% (performance decrease).
* Values range from approximately -5% (LLaMA3.1-8B) to -18% (GPT-4o).
* Cluster of points between -10% and -15% includes LLaMA3.1-70B, Qwen2.5-7B, Qwen2.5-72B, GPT-3.5, and Claude3.5.
**2. CS-QA:**
* All models show negative Δ%.
* Values range from approximately -12% (Claude3.5) to -25% (LLaMA3.1-8B).
* Most models cluster between -15% and -20%.
**3. AQUA:**
* All models show negative Δ%.
* Significant outlier: LLaMA3.1-70B shows the largest decrease at approximately -31%.
* Other models range from approximately -1% (Qwen2.5-7B) to -18% (Qwen2.5-72B).
**4. GSM8K:**
* All models show negative Δ%.
* Values range from approximately -6% (GPT-3.5) to -29% (Qwen2.5-7B).
* DeepSeek-V3 shows a decrease of approximately -25%.
**5. MATH:**
* All models show negative Δ%.
* Values range from approximately -2% (Claude3.5) to -21% (DeepSeek-V3).
* Most models cluster between -10% and -15%.
**6. GPQA:**
* Mixed performance. Some models show positive Δ%, others negative.
* **Positive Δ%:** Claude3.5 (~+10%), DeepSeek-V3 (~+11%), Qwen2.5-72B (~+1%).
* **Near Baseline:** LLaMA3.1-8B (~-3%), GPT-3.5 (~0%).
* **Negative Δ%:** LLaMA3.1-70B (~-5%), GPT-4o (~-6%), QWQ-32B (~-2%).
**7. HumanEval:**
* Data is sparse. Only two data points are clearly visible.
* Qwen2.5-72B: Approximately -2%.
* DeepSeek-V3: Approximately -2%.
* Other models are not plotted for this dataset.
### Key Observations
1. **Predominant Negative Trend:** Across the first five datasets (HotpotQA through MATH), every single model shows a negative performance change (Δ < 0), indicating a consistent decrease in performance on these benchmarks.
2. **GPQA as an Exception:** GPQA is the only dataset where multiple models (Claude3.5, DeepSeek-V3, Qwen2.5-72B) show a positive performance change.
3. **Model Performance Variability:**
* **LLaMA3.1-70B** shows extreme variability, with the largest decrease on AQUA (~-31%) but a relatively smaller decrease on GPQA (~-5%).
* **Claude3.5** and **DeepSeek-V3** (both "Reasoning LLMs" per the legend) are the top performers on GPQA, showing the only significant positive gains.
* **Qwen2.5-7B** shows one of the largest decreases on GSM8K (~-29%).
4. **Symbol/Category Correlation:** The legend categorizes models by symbol shape: circles for "Open LLM", pentagons for "Close LLM", and diamonds for "Reasoning LLM". This categorization is visually consistent throughout the plot.
### Interpretation
This chart likely illustrates the performance delta of various LLMs when subjected to a specific intervention, condition, or evaluation method compared to a baseline. The consistent negative Δ% across most datasets suggests the intervention generally hinders performance on tasks like question answering (HotpotQA, CS-QA, AQUA) and mathematical reasoning (GSM8K, MATH).
The notable exception is the GPQA dataset, where "Reasoning LLMs" (Claude3.5, DeepSeek-V3) and one other model (Qwen2.5-72B) show improved performance. This suggests the intervention or evaluation condition may specifically benefit certain model architectures or training paradigms on this particular type of task (GPQA is a graduate-level science QA benchmark).
The extreme negative outlier for LLaMA3.1-70B on AQUA indicates a severe and specific failure mode for that model under the tested condition. The sparse data for HumanEval limits conclusions but shows minimal negative impact for the two models plotted.
In summary, the data demonstrates that the evaluated condition has a broadly negative impact on LLM performance across diverse benchmarks, with a specific, positive exception for a subset of models on the GPQA dataset. This highlights the importance of evaluating model robustness across multiple domains, as performance impacts can be highly dataset- and model-specific.
