\n ## Line Charts: Accuracy vs. Noise Intensity Coefficient for Different Models ### Overview The image presents three separate line charts, each depicting the relationship between Accuracy (Acc(%) on the y-axis) and Noise Intensity Coefficient (ω) on the x-axis. Each chart represents a different model: Llama3-8B, Qwen3-8B, and Qwen3-4B. Within each chart, two lines represent different training methods: "DiffCoT" (blue line) and "Full-Step DPO" (orange line). The charts aim to compare the robustness of these models and training methods to increasing levels of noise. ### Components/Axes * **X-axis Label (all charts):** "Noise Intensity Coefficient ω" with markers at 0.0, 0.1, 0.2, 0.3, and 0.4. * **Y-axis Label (all charts):** "Acc(%)" with a scale ranging from approximately 15% to 70%. * **Chart Titles:** * Left: "Llama3-8B" * Center: "Qwen3-8B" * Right: "Qwen3-4B" * **Legend (bottom-left of each chart):** * Blue Line: "DiffCoT" * Orange Line: "Full-Step DPO" ### Detailed Analysis or Content Details **Llama3-8B (Left Chart):** * The "DiffCoT" line (blue) starts at approximately 38% accuracy at ω = 0.0 and decreases linearly to approximately 19% accuracy at ω = 0.4. * The "Full-Step DPO" line (orange) starts at approximately 36% accuracy at ω = 0.0 and decreases more steeply to approximately 17% accuracy at ω = 0.4. **Qwen3-8B (Center Chart):** * The "DiffCoT" line (blue) starts at approximately 67% accuracy at ω = 0.0 and decreases linearly to approximately 44% accuracy at ω = 0.4. * The "Full-Step DPO" line (orange) starts at approximately 65% accuracy at ω = 0.0 and decreases more steeply to approximately 40% accuracy at ω = 0.4. **Qwen3-4B (Right Chart):** * The "DiffCoT" line (blue) starts at approximately 63% accuracy at ω = 0.0 and decreases linearly to approximately 48% accuracy at ω = 0.4. * The "Full-Step DPO" line (orange) starts at approximately 62% accuracy at ω = 0.0 and decreases more steeply to approximately 38% accuracy at ω = 0.4. ### Key Observations * In all three charts, both training methods show a decrease in accuracy as the noise intensity coefficient increases. * "Full-Step DPO" consistently exhibits a steeper decline in accuracy compared to "DiffCoT" across all models and noise levels. * Qwen3-8B and Qwen3-4B generally achieve higher initial accuracy levels (at ω = 0.0) than Llama3-8B. * The rate of accuracy decline appears relatively consistent for "DiffCoT" across all models. ### Interpretation The data suggests that increasing noise negatively impacts the accuracy of all three models, regardless of the training method used. "Full-Step DPO" appears to be more sensitive to noise than "DiffCoT," as evidenced by the steeper decline in accuracy. This could indicate that "DiffCoT" provides a more robust training approach in the presence of noisy data. The higher initial accuracy of the Qwen models suggests they may have a stronger baseline performance compared to Llama3-8B. The linear relationship between noise intensity and accuracy decline suggests a predictable degradation in performance as noise levels increase. The consistent trends across all three models indicate that the observed behavior is likely not model-specific but rather a general characteristic of these types of language models when exposed to noise. The charts provide a comparative analysis of model robustness and training method effectiveness under varying noise conditions.