## Line Charts: Model Accuracy Comparison ### Overview The image presents three line charts comparing the accuracy of different models (Llama-3, Mistral, and Gemma-2) across several rounds of interaction. Each chart displays the performance of various training methods, including SoM (2x), SoM (4x), Persona, DebateTune, SFT, DebateGPT, ACC-Collab (Ours), and ACC-Collab + (Ours). The x-axis represents the round number (0 to 4), and the y-axis represents the accuracy, ranging from approximately 0.62 to 0.68 for Llama-3 and Mistral, and from 0.48 to 0.58 for Gemma-2. Each line represents a different training method, with shaded areas indicating the confidence interval or variability in the accuracy. ### Components/Axes * **Titles:** * Top-Left Chart: "Llama-3" * Top-Middle Chart: "Mistral" * Top-Right Chart: "Gemma-2" * **X-Axis:** * Label: "Round" * Scale: 0, 1, 2, 3, 4 * **Y-Axis:** * Label: "Accuracy" * Left Chart Scale: 0.62, 0.64, 0.66, 0.68 * Middle Chart Scale: 0.56, 0.58, 0.60, 0.62, 0.64, 0.66, 0.68 * Right Chart Scale: 0.48, 0.50, 0.52, 0.54, 0.56, 0.58 * **Legend:** Located at the bottom of the image. * Blue Dashed Line: "SoM (2x)" * Teal Dashed Line: "SoM (4x)" * Purple Dashed Line: "Persona" * Brown Solid Line: "DebateTune" * Light Green Solid Line: "SFT" * Dark Green Solid Line: "DebateGPT" * Orange Solid Line: "ACC-Collab (Ours)" * Red Solid Line: "ACC-Collab + (Ours)" ### Detailed Analysis **Llama-3 Chart:** * **SoM (2x) (Blue Dashed):** Starts at approximately 0.61 and increases slightly to around 0.62, remaining relatively flat. * **SoM (4x) (Teal Dashed):** Starts at approximately 0.61 and increases slightly to around 0.64, remaining relatively flat. * **Persona (Purple Dashed):** Starts at approximately 0.62 and increases slightly to around 0.64, remaining relatively flat. * **DebateTune (Brown Solid):** Starts at approximately 0.62 and increases slightly to around 0.63, remaining relatively flat. * **SFT (Light Green Solid):** Starts at approximately 0.58 and increases to approximately 0.65 by round 3, then decreases slightly to around 0.64 by round 4. * **DebateGPT (Dark Green Solid):** Starts at approximately 0.64 and increases to approximately 0.65 by round 3, then decreases slightly to around 0.64 by round 4. * **ACC-Collab (Ours) (Orange Solid):** Starts at approximately 0.63 and increases to approximately 0.65 by round 3, then decreases slightly to around 0.64 by round 4. * **ACC-Collab + (Ours) (Red Solid):** Starts at approximately 0.64 and increases to approximately 0.67 by round 1, then increases to approximately 0.67 by round 2, then increases to approximately 0.67 by round 3, then increases to approximately 0.68 by round 4. **Mistral Chart:** * **SoM (2x) (Blue Dashed):** Starts at approximately 0.56 and increases slightly to around 0.57, remaining relatively flat. * **SoM (4x) (Teal Dashed):** Starts at approximately 0.55 and increases slightly to around 0.57, remaining relatively flat. * **Persona (Purple Dashed):** Starts at approximately 0.55 and increases slightly to around 0.57, remaining relatively flat. * **DebateTune (Brown Solid):** Starts at approximately 0.55 and increases slightly to around 0.57, remaining relatively flat. * **SFT (Light Green Solid):** Starts at approximately 0.58 and increases to approximately 0.60 by round 1, then decreases slightly to around 0.59 by round 4. * **DebateGPT (Dark Green Solid):** Starts at approximately 0.57 and increases to approximately 0.58 by round 1, then decreases slightly to around 0.57 by round 4. * **ACC-Collab (Ours) (Orange Solid):** Starts at approximately 0.59 and increases to approximately 0.61 by round 1, then increases to approximately 0.60 by round 3, then increases to approximately 0.61 by round 4. * **ACC-Collab + (Ours) (Red Solid):** Starts at approximately 0.64 and increases to approximately 0.67 by round 1, then increases to approximately 0.67 by round 2, then increases to approximately 0.67 by round 3, then increases to approximately 0.67 by round 4. **Gemma-2 Chart:** * **SoM (2x) (Blue Dashed):** Starts at approximately 0.57 and increases slightly to around 0.58, remaining relatively flat. * **SoM (4x) (Teal Dashed):** Starts at approximately 0.55 and increases slightly to around 0.57, remaining relatively flat. * **Persona (Purple Dashed):** Starts at approximately 0.55 and increases slightly to around 0.57, remaining relatively flat. * **DebateTune (Brown Solid):** Starts at approximately 0.55 and increases slightly to around 0.57, remaining relatively flat. * **SFT (Light Green Solid):** Starts at approximately 0.57 and increases to approximately 0.57 by round 1, then increases to approximately 0.57 by round 4. * **DebateGPT (Dark Green Solid):** Starts at approximately 0.57 and increases to approximately 0.57 by round 1, then increases to approximately 0.57 by round 4. * **ACC-Collab (Ours) (Orange Solid):** Starts at approximately 0.54 and decreases to approximately 0.50 by round 1, then decreases to approximately 0.50 by round 3, then increases to approximately 0.51 by round 4. * **ACC-Collab + (Ours) (Red Solid):** Starts at approximately 0.51 and increases to approximately 0.54 by round 1, then decreases to approximately 0.54 by round 2, then decreases to approximately 0.54 by round 3, then increases to approximately 0.55 by round 4. ### Key Observations * **ACC-Collab + (Ours) (Red Solid):** Consistently shows the highest accuracy across all three models (Llama-3, Mistral, and Gemma-2). * **SoM (2x) (Blue Dashed), SoM (4x) (Teal Dashed), Persona (Purple Dashed), and DebateTune (Brown Solid):** Generally exhibit lower and relatively stable accuracy across all rounds and models. * **SFT (Light Green Solid) and DebateGPT (Dark Green Solid):** Show moderate performance, with some variation across different models. * **ACC-Collab (Ours) (Orange Solid):** Shows variable performance, with a noticeable dip in accuracy for Gemma-2. ### Interpretation The charts provide a comparative analysis of different training methods applied to three language models (Llama-3, Mistral, and Gemma-2). The "ACC-Collab + (Ours)" method consistently outperforms other methods, suggesting it is the most effective in improving model accuracy across different architectures. The relatively flat performance of "SoM (2x)", "SoM (4x)", "Persona", and "DebateTune" indicates that these methods may not be as effective in enhancing accuracy for these models. The variability in "ACC-Collab (Ours)" performance, particularly the dip in accuracy for Gemma-2, suggests that its effectiveness may be model-dependent. Overall, the data suggests that the choice of training method significantly impacts the final accuracy of the language model, with "ACC-Collab + (Ours)" being a promising approach.

\n ## Line Chart: Accuracy vs. Round for Different Models and Training Methods ### Overview This image presents three line charts, each displaying the accuracy of different language models (Llama-3, Mistral, and Gemma-2) across four rounds of evaluation. Each chart shows the performance of various training methods applied to the respective model. The accuracy is plotted on the y-axis, and the round number is on the x-axis. Shaded areas around each line represent confidence intervals. ### Components/Axes * **X-axis:** Round (0, 1, 2, 3, 4) * **Y-axis:** Accuracy (ranging approximately from 0.48 to 0.68) * **Models (Charts):** Llama-3, Mistral, Gemma-2 * **Training Methods (Legend):** * SoM (2x) - Dashed dark blue line * SoM (4x) - Dashed light blue line * Persona - Solid purple line * DebateTune - Solid green line * SFT - Solid orange line * DebateGPT - Solid teal line * ACC-Collab (Ours) - Solid red line * ACC-Collab + (Ours) - Dashed red line * **Legend Position:** Bottom-center of the image, spanning all three charts. ### Detailed Analysis or Content Details **Llama-3 Chart (Left):** * **ACC-Collab (Ours):** Starts at approximately 0.64, increases to around 0.67 at round 1, then fluctuates around 0.67-0.68 for rounds 2-4. * **ACC-Collab + (Ours):** Starts at approximately 0.63, increases to around 0.66 at round 1, then fluctuates around 0.66-0.67 for rounds 2-4. * **SoM (2x):** Starts at approximately 0.62, increases to around 0.64 at round 1, then remains relatively stable around 0.64-0.65 for rounds 2-4. * **SoM (4x):** Starts at approximately 0.61, increases to around 0.63 at round 1, then remains relatively stable around 0.63-0.64 for rounds 2-4. * **Persona:** Starts at approximately 0.63, increases to around 0.65 at round 1, then remains relatively stable around 0.65-0.66 for rounds 2-4. * **DebateTune:** Starts at approximately 0.62, increases to around 0.65 at round 1, then remains relatively stable around 0.65-0.66 for rounds 2-4. * **SFT:** Starts at approximately 0.62, increases to around 0.65 at round 1, then remains relatively stable around 0.65-0.66 for rounds 2-4. * **DebateGPT:** Starts at approximately 0.62, increases to around 0.65 at round 1, then remains relatively stable around 0.65-0.66 for rounds 2-4. **Mistral Chart (Center):** * **ACC-Collab (Ours):** Starts at approximately 0.67, decreases to around 0.66 at round 1, then fluctuates around 0.66-0.67 for rounds 2-4. * **ACC-Collab + (Ours):** Starts at approximately 0.65, increases to around 0.66 at round 1, then fluctuates around 0.66-0.67 for rounds 2-4. * **SoM (2x):** Starts at approximately 0.56, increases to around 0.58 at round 1, then remains relatively stable around 0.58-0.59 for rounds 2-4. * **SoM (4x):** Starts at approximately 0.56, increases to around 0.58 at round 1, then remains relatively stable around 0.58-0.59 for rounds 2-4. * **Persona:** Starts at approximately 0.57, increases to around 0.59 at round 1, then remains relatively stable around 0.59-0.60 for rounds 2-4. * **DebateTune:** Starts at approximately 0.58, increases to around 0.60 at round 1, then remains relatively stable around 0.60-0.61 for rounds 2-4. * **SFT:** Starts at approximately 0.58, increases to around 0.60 at round 1, then remains relatively stable around 0.60-0.61 for rounds 2-4. * **DebateGPT:** Starts at approximately 0.58, increases to around 0.60 at round 1, then remains relatively stable around 0.60-0.61 for rounds 2-4. **Gemma-2 Chart (Right):** * **ACC-Collab (Ours):** Starts at approximately 0.50, increases to around 0.54 at round 1, then fluctuates around 0.55-0.56 for rounds 2-4. * **ACC-Collab + (Ours):** Starts at approximately 0.49, increases to around 0.56 at round 1, then remains relatively stable around 0.56-0.57 for rounds 2-4. * **SoM (2x):** Starts at approximately 0.52, increases to around 0.55 at round 1, then remains relatively stable around 0.55-0.56 for rounds 2-4. * **SoM (4x):** Starts at approximately 0.52, increases to around 0.55 at round 1, then remains relatively stable around 0.55-0.56 for rounds 2-4. * **Persona:** Starts at approximately 0.53, increases to around 0.56 at round 1, then remains relatively stable around 0.56-0.57 for rounds 2-4. * **DebateTune:** Starts at approximately 0.53, increases to around 0.56 at round 1, then remains relatively stable around 0.56-0.57 for rounds 2-4. * **SFT:** Starts at approximately 0.53, increases to around 0.56 at round 1, then remains relatively stable around 0.56-0.57 for rounds 2-4. * **DebateGPT:** Starts at approximately 0.53, increases to around 0.56 at round 1, then remains relatively stable around 0.56-0.57 for rounds 2-4. ### Key Observations * **Llama-3** consistently exhibits the highest accuracy across all training methods and rounds. * **Mistral** shows moderate accuracy, generally lower than Llama-3 but higher than Gemma-2. * **Gemma-2** has the lowest overall accuracy. * For all models, accuracy generally increases from round 0 to round 1, then plateaus or fluctuates slightly in subsequent rounds. * The "ACC-Collab (Ours)" method generally performs well across all models, often achieving the highest accuracy. * The "ACC-Collab + (Ours)" method shows similar performance to "ACC-Collab (Ours)". ### Interpretation The charts demonstrate the effectiveness of different training methods on various language models. Llama-3 appears to be the most capable model, achieving significantly higher accuracy than Mistral and Gemma-2. The "ACC-Collab" training approach consistently yields strong results, suggesting its suitability for improving model performance. The initial accuracy gains observed from round 0 to round 1 indicate that the training process is effective in the early stages, but further rounds yield diminishing returns. The confidence intervals (shaded areas) suggest some variability in the results, but the overall trends are clear. The data suggests that model choice and training method are both crucial factors in achieving high accuracy in language models. The relatively stable performance after round 1 could indicate that the models are approaching their performance limits with the given training data and methods.

## Multi-Panel Line Chart: Accuracy of Various Methods Across Iterative Rounds ### Overview The image displays three side-by-side line charts comparing the performance (accuracy) of eight different methods over a series of 0 to 4 iterative rounds. Each chart corresponds to a different base language model: Llama-3, Mistral, and Gemma-2. The charts share a common legend and x-axis label but have independent y-axis scales. Shaded regions around each line indicate confidence intervals or variance. ### Components/Axes * **Chart Titles (Top Center):** "Llama-3" (left), "Mistral" (center), "Gemma-2" (right). * **X-Axis (Bottom of each chart):** Labeled "Round". Ticks are at integer values 0, 1, 2, 3, 4. * **Y-Axis (Left of each chart):** Labeled "Accuracy". The scale varies: * Llama-3: ~0.62 to ~0.69 * Mistral: ~0.56 to ~0.68 * Gemma-2: ~0.48 to ~0.58 * **Legend (Bottom, spanning all charts):** Contains 8 entries, each with a distinct color and line style: 1. **SoM (2x):** Blue dashed line (`--`). 2. **SoM (4x):** Teal dashed line (`--`). 3. **Persona:** Purple dashed line (`--`). 4. **DebateTune:** Brown solid line. 5. **SFT:** Light green solid line. 6. **DebateGPT:** Dark green solid line. 7. **ACC-Collab (Ours):** Orange solid line with circular markers. 8. **ACC-Collab + (Ours):** Red solid line with circular markers. ### Detailed Analysis **1. Llama-3 Chart (Left Panel)** * **Trend Verification:** The red line (ACC-Collab +) shows a strong, consistent upward trend. The orange line (ACC-Collab) also trends upward but with a dip at round 4. The dark green (DebateGPT) and light green (SFT) lines show moderate upward trends. The dashed lines (SoM variants, Persona) and the brown line (DebateTune) are relatively flat or show very slight improvement. * **Data Points (Approximate Accuracy):** * **ACC-Collab + (Red):** R0: ~0.640, R1: ~0.668, R2: ~0.669, R3: ~0.668, R4: ~0.683. * **ACC-Collab (Orange):** R0: ~0.635, R1: ~0.640, R2: ~0.648, R3: ~0.656, R4: ~0.644. * **DebateGPT (Dark Green):** R0: ~0.640, R1: ~0.649, R2: ~0.652, R3: ~0.653, R4: ~0.654. * **SFT (Light Green):** R0: ~0.640, R1: ~0.640, R2: ~0.640, R3: ~0.640, R4: ~0.642. * **DebateTune (Brown):** R0: ~0.620, R1: ~0.631, R2: ~0.632, R3: ~0.631, R4: ~0.630. * **Persona (Purple Dashed):** R0: ~0.622, R1: ~0.633, R2: ~0.638, R3: ~0.636, R4: ~0.640. * **SoM (4x) (Teal Dashed):** R0: ~0.615, R1: ~0.629, R2: ~0.632, R3: ~0.635, R4: ~0.635. * **SoM (2x) (Blue Dashed):** R0: ~0.617, R1: ~0.620, R2: ~0.620, R3: ~0.620, R4: ~0.620. **2. Mistral Chart (Center Panel)** * **Trend Verification:** The red line (ACC-Collab +) shows a sharp initial rise from R0 to R1, then plateaus with a slight upward drift. The orange line (ACC-Collab) shows a steady, moderate upward trend. The light green (SFT) line rises initially then plateaus. The dark green (DebateGPT), brown (DebateTune), and dashed lines are clustered at the bottom with minimal change. * **Data Points (Approximate Accuracy):** * **ACC-Collab + (Red):** R0: ~0.638, R1: ~0.670, R2: ~0.662, R3: ~0.670, R4: ~0.672. * **ACC-Collab (Orange):** R0: ~0.586, R1: ~0.597, R2: ~0.605, R3: ~0.604, R4: ~0.610. * **SFT (Light Green):** R0: ~0.572, R1: ~0.589, R2: ~0.592, R3: ~0.594, R4: ~0.594. * **DebateGPT (Dark Green):** R0: ~0.558, R1: ~0.577, R2: ~0.573, R3: ~0.576, R4: ~0.578. * **DebateTune (Brown):** R0: ~0.553, R1: ~0.560, R2: ~0.561, R3: ~0.563, R4: ~0.563. * **Persona (Purple Dashed):** R0: ~0.553, R1: ~0.570, R2: ~0.571, R3: ~0.572, R4: ~0.573. * **SoM (4x) (Teal Dashed):** R0: ~0.560, R1: ~0.570, R2: ~0.571, R3: ~0.572, R4: ~0.573. * **SoM (2x) (Blue Dashed):** R0: ~0.560, R1: ~0.570, R2: ~0.571, R3: ~0.572, R4: ~0.573. **3. Gemma-2 Chart (Right Panel)** * **Trend Verification:** The red line (ACC-Collab +) shows a clear upward trend. The orange line (ACC-Collab) is highly volatile, with a sharp drop at R1, a peak at R2, another drop at R3, and a recovery at R4. The cluster of lines at the top (DebateGPT, SFT, DebateTune, Persona, SoM variants) are tightly grouped and show a very slight, steady upward trend. * **Data Points (Approximate Accuracy):** * **ACC-Collab + (Red):** R0: ~0.511, R1: ~0.531, R2: ~0.544, R3: ~0.538, R4: ~0.555. * **ACC-Collab (Orange):** R0: ~0.541, R1: ~0.497, R2: ~0.514, R3: ~0.498, R4: ~0.510. * **Top Cluster (R0 values ~0.560-0.566, R4 values ~0.576-0.581):** This group includes DebateGPT (Dark Green), SFT (Light Green), DebateTune (Brown), Persona (Purple Dashed), SoM (4x) (Teal Dashed), and SoM (2x) (Blue Dashed). Their lines are nearly indistinguishable at this scale, all trending gently upward from ~0.56 to ~0.58. ### Key Observations 1. **Consistent Superiority:** The "ACC-Collab + (Ours)" method (red line) achieves the highest or near-highest accuracy in all three models by Round 4. 2. **Model-Dependent Performance:** The relative performance of methods varies significantly by base model. For example, the "top cluster" in Gemma-2 performs similarly to the mid-tier methods in Llama-3 and Mistral. 3. **Volatility:** The "ACC-Collab (Ours)" method (orange line) shows notable volatility, especially in the Gemma-2 chart, suggesting its performance may be less stable. 4. **Diminishing Returns:** Most methods show the most significant gains between Round 0 and Round 1, with improvements slowing or plateauing in later rounds. 5. **Baseline Clustering:** The baseline methods (SoM variants, Persona, DebateTune) often cluster together at the lower end of the performance spectrum, particularly in the Mistral and Llama-3 charts. ### Interpretation This data suggests that the proposed collaborative methods, particularly the enhanced "ACC-Collab +", are effective at improving model accuracy through iterative rounds of debate or collaboration. The consistent upward trend of the red line indicates that the method successfully leverages multiple rounds to refine answers. The variation across base models (Llama-3, Mistral, Gemma-2) implies that the effectiveness of these collaborative techniques is influenced by the underlying capabilities or characteristics of the base model. The tight clustering of baselines in Gemma-2 might indicate a performance ceiling for those methods on that specific model, which the ACC-Collab methods are able to break through. The volatility of the standard "ACC-Collab" method, contrasted with the stability of "ACC-Collab +", highlights the importance of the specific enhancements made in the "+" version. These enhancements likely add robustness to the collaborative process. Overall, the charts provide strong evidence for the value of iterative, structured collaboration (like debate) for improving LLM performance on the measured task, with the proposed "ACC-Collab +" framework being the most effective approach shown. The confidence intervals (shaded areas) are generally wider for the top-performing methods, indicating greater variance in their outcomes, which is a trade-off for their higher average performance.

## Line Graphs: Model Performance Across Rounds ### Overview The image contains three line graphs comparing the accuracy of different AI models across four rounds of evaluation. Each graph represents a different base model (Llama-3, Mistral, Gemma-2), with performance metrics tracked for multiple fine-tuning approaches. The graphs use colored lines with shaded confidence intervals to visualize performance trends. ### Components/Axes - **X-axis**: Round (0 to 4, integer values) - **Y-axis**: Accuracy (0.54 to 0.68, decimal values) - **Legends**: - **Llama-3**: - Blue dashed: SoM (2x) - Teal dashed: SoM (4x) - Purple dashed: Persona - Brown solid: DebateTune - Green solid: SIFT - Orange solid: DebateGPT - Red solid: ACC-Collab (Ours) - Red solid with gradient: ACC-Collab + (Ours) - **Mistral**: Same legend as Llama-3 - **Gemma-2**: Same legend as Llama-3 - **Graph Titles**: Llama-3 (top-left), Mistral (top-center), Gemma-2 (top-right) ### Detailed Analysis #### Llama-3 - **ACC-Collab (Ours)**: Starts at ~0.63 (Round 0), peaks at ~0.68 (Round 4) - **ACC-Collab + (Ours)**: Starts at ~0.62 (Round 0), peaks at ~0.68 (Round 4) - **Other Models**: - DebateGPT (green) shows steady growth from ~0.64 to ~0.66 - SIFT (yellow) fluctuates between ~0.63-0.65 - SoM (2x/4x) and Persona (purple) remain below 0.64 #### Mistral - **ACC-Collab (Ours)**: Starts at ~0.64 (Round 0), peaks at ~0.67 (Round 1), then stabilizes - **ACC-Collab + (Ours)**: Starts at ~0.63 (Round 0), peaks at ~0.67 (Round 4) - **Other Models**: - DebateGPT (green) grows from ~0.56 to ~0.58 - SIFT (yellow) increases from ~0.57 to ~0.60 - SoM (2x/4x) and Persona (purple) remain below 0.60 #### Gemma-2 - **ACC-Collab (Ours)**: Starts at ~0.54 (Round 0), dips to ~0.52 (Round 1), peaks at ~0.56 (Round 4) - **ACC-Collab + (Ours)**: Starts at ~0.56 (Round 0), dips to ~0.54 (Round 1), peaks at ~0.57 (Round 4) - **Other Models**: - DebateGPT (green) shows steady growth from ~0.56 to ~0.58 - SIFT (yellow) increases from ~0.57 to ~0.59 - SoM (2x/4x) and Persona (purple) remain below 0.56 ### Key Observations 1. **ACC-Collab + (Ours)** consistently outperforms **ACC-Collab (Ours)** in later rounds across all base models 2. **Gemma-2** shows the most significant performance gap between the two ACC-Collab variants 3. **DebateGPT** and **SIFT** demonstrate gradual improvement across all base models 4. **SoM (4x)** and **Persona** show minimal growth compared to other methods 5. Confidence intervals (shaded areas) indicate higher variability in Mistral's performance ### Interpretation The data suggests that combining ACC-Collab with additional optimization techniques ("Ours") enhances model performance, particularly in later evaluation rounds. This trend is most pronounced in Mistral and Gemma-2, where ACC-Collab + (Ours) achieves 2-3% higher accuracy than the base ACC-Collab approach. The gradual improvement of DebateGPT and SIFT across all models indicates their effectiveness as fine-tuning strategies, though they don't match the peak performance of ACC-Collab variants. The stability of SoM and Persona methods suggests they may be less sensitive to iterative refinement. The widening confidence intervals in Mistral's graph highlight potential instability in its evaluation process compared to the other models.