## Line Chart: Model Accuracy vs. Number of Interactions ### Overview The image displays three subplots comparing the accuracy trends of five models (Original LLM, Oracle LLM, Bayesian LLM, Bayesian Assistant, and Random) across three datasets (Gemma, Llama, Owen). Accuracy (%) is plotted against the number of interactions (0–4), with distinct color-coded lines for each model. --- ### Components/Axes - **X-axis**: Number of Interactions (0–4, integer steps) - **Y-axis**: Accuracy (%) (0–100, linear scale) - **Legend**: Located at the top, with color-coded labels: - Blue: Original LLM - Orange: Oracle LLM - Brown: Bayesian LLM - Gray: Bayesian Assistant - Dashed Gray: Random - **Subplots**: Three separate plots labeled "Gemma," "Llama," and "Owen" (left to right). --- ### Detailed Analysis #### Gemma Subplot - **Original LLM (Blue)**: Starts at ~60%, remains stable (~60–62%) across all interactions. - **Oracle LLM (Orange)**: Begins at ~30%, rises steadily to ~60% by interaction 4. - **Bayesian LLM (Brown)**: Starts at ~20%, increases sharply to ~60% by interaction 4. - **Bayesian Assistant (Gray)**: Begins at ~30%, rises to ~55% by interaction 4. - **Random (Dashed Gray)**: Flat at ~30% across all interactions. #### Llama Subplot - **Original LLM (Blue)**: Starts at ~60%, dips to ~55% at interaction 1, then stabilizes (~55–60%). - **Oracle LLM (Orange)**: Begins at ~30%, rises to ~50% by interaction 4. - **Bayesian LLM (Brown)**: Starts at ~20%, jumps to ~50% at interaction 2, remains stable. - **Bayesian Assistant (Gray)**: Begins at ~30%, peaks at ~55% at interaction 3, then drops slightly. - **Random (Dashed Gray)**: Flat at ~30%. #### Owen Subplot - **Original LLM (Blue)**: Starts at ~60%, drops to ~50% at interaction 1, then stabilizes (~50–55%). - **Oracle LLM (Orange)**: Begins at ~30%, rises to ~45% by interaction 4. - **Bayesian LLM (Brown)**: Starts at ~20%, jumps to ~55% at interaction 2, remains stable. - **Bayesian Assistant (Gray)**: Begins at ~30%, peaks at ~55% at interaction 4. - **Random (Dashed Gray)**: Flat at ~30%. --- ### Key Observations 1. **Bayesian Models Outperform**: Bayesian LLM and Bayesian Assistant consistently show the steepest improvement across all datasets, surpassing other models by interaction 2–4. 2. **Oracle LLM Improves Gradually**: Oracle LLM demonstrates steady gains but lags behind Bayesian models. 3. **Original LLM Stability**: Original LLM maintains relatively stable performance, with minor fluctuations. 4. **Random Baseline**: The Random model serves as a consistent lower bound (~30%) across all datasets. 5. **Dataset Variability**: - Gemma shows the most pronounced improvement for Bayesian models. - Owen exhibits the largest drop in Original LLM performance at interaction 1. --- ### Interpretation The data suggests that **Bayesian methods (LLM and Assistant)** are highly effective at improving accuracy with increased interactions, outperforming non-Bayesian models. The Oracle LLM also benefits from interactions but to a lesser extent. The Original LLM’s performance is less sensitive to interaction count, while the Random model acts as a static baseline. Notably, the Bayesian Assistant’s peak at interaction 3 in Llama and interaction 4 in Owen indicates a delayed but significant improvement. These trends highlight the importance of interaction count in model refinement, particularly for Bayesian approaches.