## Grouped Bar Charts: Language Model Performance (WinRate & ASR) with Security Methods
### Overview
The image contains three grouped bar charts, each analyzing a language model (**Llama-7B**, **Mistral-7B**, **Llama3-8B**) across three metrics. Each chart compares three methods: *None* (gray), *StruQ* (blue), and *SecAlign* (orange). The y-axis measures percentage (0–100), and the x-axis includes:
- `AlpacaEval2 WinRate (↑)` (higher = better performance),
- `Max ASR (↓) Opt.-Free` (lower = better security),
- `Max ASR (↓) Opt.-Based` (lower = better security).
### Components/Axes
- **Y-axis**: `WinRate / ASR (%)` (scale: 0, 20, 40, 60, 80, 100).
- **X-axis (per subplot)**: Three categories (WinRate, Opt.-Free ASR, Opt.-Based ASR).
- **Legend**: Gray = *None*, Blue = *StruQ*, Orange = *SecAlign*.
- **Subplot Titles**: Left = *Llama-7B*, Middle = *Mistral-7B*, Right = *Llama3-8B*.
### Detailed Analysis (Per Subplot)
#### 1. Llama-7B (Left Subplot)
- **AlpacaEval2 WinRate (↑)**: All three methods have similar WinRates (~55–60%).
- **Max ASR (↓) Opt.-Free**:
- *None* (gray): ~75% (tall bar).
- *StruQ* (blue): ~0% (near-zero).
- *SecAlign* (orange): ~0% (near-zero).
- **Max ASR (↓) Opt.-Based**:
- *None* (gray): ~95% (tallest bar).
- *StruQ* (blue): ~60% (medium height).
- *SecAlign* (orange): ~15% (short bar).
#### 2. Mistral-7B (Middle Subplot)
- **AlpacaEval2 WinRate (↑)**: All three methods have similar WinRates (~70%).
- **Max ASR (↓) Opt.-Free**:
- *None* (gray): ~90% (tall bar).
- *StruQ* (blue): ~0% (near-zero).
- *SecAlign* (orange): ~0% (near-zero).
- **Max ASR (↓) Opt.-Based**:
- *None* (gray): ~95% (tallest bar).
- *StruQ* (blue): ~40% (medium height).
- *SecAlign* (orange): ~0% (near-zero).
#### 3. Llama3-8B (Right Subplot)
- **AlpacaEval2 WinRate (↑)**: All three methods have similar WinRates (~70%).
- **Max ASR (↓) Opt.-Free**:
- *None* (gray): ~90% (tall bar).
- *StruQ* (blue): ~0% (near-zero).
- *SecAlign* (orange): ~0% (near-zero).
- **Max ASR (↓) Opt.-Based**:
- *None* (gray): ~95% (tallest bar).
- *StruQ* (blue): ~40% (medium height).
- *SecAlign* (orange): ~10% (short bar).
### Key Observations
- **WinRate Consistency**: For all models, *None*, *StruQ*, and *SecAlign* yield nearly identical AlpacaEval2 WinRates (no performance tradeoff for security).
- **ASR (Opt.-Free) Reduction**: *StruQ* and *SecAlign* reduce Max ASR (Opt.-Free) to ~0% (drastic security improvement vs. *None*).
- **ASR (Opt.-Based) Variation**: *SecAlign* outperforms *StruQ* in reducing Opt.-Based ASR (e.g., ~10–15% for Llama-7B/Llama3-8B, ~0% for Mistral-7B).
- **Model Differences**: Llama-7B has lower baseline WinRate (~55%) and lower Opt.-Free ASR for *None* (~75%) than Mistral-7B/Llama3-8B (~70% WinRate, ~90% Opt.-Free ASR).
### Interpretation
- **Security vs. Performance**: *StruQ* and *SecAlign* improve security (lower ASR) without sacrificing performance (consistent WinRate), making them effective for robust model deployment.
- **Method Efficacy**: *SecAlign* is more effective than *StruQ* for Opt.-Based ASR reduction, suggesting it better mitigates adversarial attacks in optimized scenarios.
- **Model Vulnerability**: Llama-7B is less vulnerable in the Opt.-Free scenario (lower *None* ASR) but less performant (lower WinRate) than Mistral-7B/Llama3-8B.
This analysis enables reconstruction of the image’s data, trends, and implications for language model security and performance.
