## Line Chart: Accuracy vs. Round for Different Models and Training Methods ### Overview The 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 several training methods: SoM (2x and 4x), Persona, DebateTune, SFT, DebateGPT, and ACC-Collab (Ours) and ACC-Collab + (Ours). The y-axis represents accuracy, and the x-axis represents the round number. ### Components/Axes * **X-axis:** "Round" with values 0, 1, 2, 3, and 4. * **Y-axis:** "Accuracy" with a scale ranging from approximately 0.44 to 0.60. * **Models (Charts):** Llama-3, Mistral, Gemma-2. Each model has its own dedicated chart. * **Training Methods (Legend):** * SoM (2x) - Dashed light blue line * SoM (4x) - Dashed dark blue line * Persona - Dashed purple line * DebateTune - Solid yellow line * SFT - Solid dark green line * DebateGPT - Solid light green line * ACC-Collab (Ours) - Solid orange line * ACC-Collab + (Ours) - Solid red line ### Detailed Analysis or Content Details **Llama-3 Chart:** * **ACC-Collab (Ours):** Starts at approximately 0.58, decreases slightly to 0.57 at round 1, then remains relatively stable around 0.57-0.58 through round 4. * **ACC-Collab + (Ours):** Starts at approximately 0.57, increases to a peak of approximately 0.59 at round 1, then decreases to approximately 0.57 by round 4. * **DebateGPT:** Starts at approximately 0.53, increases to approximately 0.56 at round 1, then remains relatively stable around 0.55-0.56 through round 4. * **DebateTune:** Starts at approximately 0.54, increases to approximately 0.56 at round 1, then remains relatively stable around 0.55-0.56 through round 4. * **SFT:** Starts at approximately 0.52, increases to approximately 0.54 at round 1, then remains relatively stable around 0.53-0.54 through round 4. * **Persona:** Starts at approximately 0.49, increases to approximately 0.51 at round 1, then remains relatively stable around 0.50-0.51 through round 4. * **SoM (4x):** Starts at approximately 0.49, increases to approximately 0.51 at round 1, then remains relatively stable around 0.50-0.51 through round 4. * **SoM (2x):** Starts at approximately 0.48, increases to approximately 0.50 at round 1, then remains relatively stable around 0.49-0.50 through round 4. **Mistral Chart:** * **ACC-Collab (Ours):** Starts at approximately 0.60, decreases to approximately 0.58 at round 1, then remains relatively stable around 0.58-0.60 through round 4. * **ACC-Collab + (Ours):** Starts at approximately 0.59, remains relatively stable around 0.58-0.59 through round 4. * **DebateGPT:** Starts at approximately 0.49, increases to approximately 0.51 at round 1, then remains relatively stable around 0.50-0.51 through round 4. * **DebateTune:** Starts at approximately 0.48, increases to approximately 0.50 at round 1, then remains relatively stable around 0.49-0.50 through round 4. * **SFT:** Starts at approximately 0.46, increases to approximately 0.48 at round 1, then remains relatively stable around 0.47-0.48 through round 4. * **Persona:** Starts at approximately 0.45, increases to approximately 0.47 at round 1, then remains relatively stable around 0.46-0.47 through round 4. * **SoM (4x):** Starts at approximately 0.44, increases to approximately 0.46 at round 1, then remains relatively stable around 0.45-0.46 through round 4. * **SoM (2x):** Starts at approximately 0.43, increases to approximately 0.45 at round 1, then remains relatively stable around 0.44-0.45 through round 4. **Gemma-2 Chart:** * **ACC-Collab (Ours):** Starts at approximately 0.51, decreases slightly to approximately 0.50 at round 1, then remains relatively stable around 0.50-0.51 through round 4. * **ACC-Collab + (Ours):** Starts at approximately 0.50, remains relatively stable around 0.49-0.50 through round 4. * **DebateGPT:** Starts at approximately 0.48, increases to approximately 0.49 at round 1, then remains relatively stable around 0.48-0.49 through round 4. * **DebateTune:** Starts at approximately 0.47, increases to approximately 0.48 at round 1, then remains relatively stable around 0.47-0.48 through round 4. * **SFT:** Starts at approximately 0.46, increases to approximately 0.47 at round 1, then remains relatively stable around 0.46-0.47 through round 4. * **Persona:** Starts at approximately 0.45, increases to approximately 0.46 at round 1, then remains relatively stable around 0.45-0.46 through round 4. * **SoM (4x):** Starts at approximately 0.44, increases to approximately 0.45 at round 1, then remains relatively stable around 0.44-0.45 through round 4. * **SoM (2x):** Starts at approximately 0.43, increases to approximately 0.44 at round 1, then remains relatively stable around 0.43-0.44 through round 4. ### Key Observations * ACC-Collab (Ours) consistently achieves the highest accuracy across all models and rounds, although the gains are modest. * ACC-Collab + (Ours) generally performs slightly worse than ACC-Collab (Ours). * SoM (2x) and SoM (4x) consistently have the lowest accuracy across all models. * The accuracy curves for most training methods tend to plateau after round 1, indicating diminishing returns from further rounds. * Mistral consistently shows the highest overall accuracy compared to Llama-3 and Gemma-2. ### Interpretation The data suggests that the "ACC-Collab (Ours)" training method is the most effective for improving the accuracy of these language models, but the improvements are not substantial. The relatively flat accuracy curves after round 1 indicate that the models may be reaching a point of diminishing returns with further training. The differences in initial accuracy and overall performance between the models (Llama-3, Mistral, and Gemma-2) suggest inherent differences in their architectures or pre-training data. The consistently low performance of SoM (2x) and SoM (4x) suggests that this training method is less effective than the others. The slight decrease in accuracy for ACC-Collab (Ours) in Llama-3 and Gemma-2 after round 1 could indicate overfitting or the need for regularization techniques. The data highlights the importance of choosing the right training method and model for a specific task, as well as the potential for diminishing returns from continued training.