## Line Chart: Agreement with Bayesian Assistant Across LLM Variants ### Overview The image displays three line charts comparing agreement percentages between different LLM variants (Original, Oracle, Bayesian) and a Bayesian Assistant across three scenarios: Gemma, Llama, and Owen. Each subplot tracks agreement over 0-5 interactions, with distinct trends for each LLM variant. ### Components/Axes - **X-axis**: Number of Interactions (0-5, integer scale) - **Y-axis**: Agreement with Bayesian Assistant (%) (0-100, linear scale) - **Legend**: - Blue: Original LLM - Orange: Oracle LLM - Red: Bayesian LLM - **Subplots**: - Left: Gemma - Center: Llama - Right: Owen ### Detailed Analysis #### Gemma Subplot - **Original LLM**: Starts at ~35%, remains flat (~35-38%) across all interactions. - **Oracle LLM**: Begins at ~35%, rises to ~65% by interaction 1, then plateaus. - **Bayesian LLM**: Starts at ~35%, spikes to ~85% by interaction 1, remains stable (~83-85%). #### Llama Subplot - **Original LLM**: Starts at ~35%, increases to ~40% by interaction 1, then stabilizes. - **Oracle LLM**: Begins at ~40%, rises to ~65% by interaction 1, then plateaus. - **Bayesian LLM**: Starts at ~40%, spikes to ~85% by interaction 1, remains stable (~83-85%). #### Owen Subplot - **Original LLM**: Starts at ~35%, increases to ~40% by interaction 1, then stabilizes. - **Oracle LLM**: Begins at ~40%, rises to ~55% by interaction 1, then plateaus (~55-58%). - **Bayesian LLM**: Starts at ~40%, spikes to ~75% by interaction 1, then declines slightly to ~70% by interaction 5. ### Key Observations 1. **Bayesian LLM Dominance**: Across all scenarios, Bayesian LLMs achieve the highest agreement, particularly after the first interaction. 2. **Oracle LLM Performance**: Oracle LLMs show moderate improvement over Original LLMs but lag behind Bayesian variants. 3. **Original LLM Stagnation**: Original LLMs exhibit minimal improvement across interactions, remaining near baseline levels. 4. **Owen Anomaly**: Bayesian LLM in Owen subplot shows a post-interaction-1 decline, unlike other scenarios. ### Interpretation The data suggests Bayesian LLMs are most effective at aligning with the Bayesian Assistant, likely due to their probabilistic reasoning framework. Oracle LLMs demonstrate partial adaptation but lack the full Bayesian optimization. Original LLMs show no meaningful improvement, indicating limited capacity for dynamic adjustment. The Owen subplot's Bayesian decline may reflect scenario-specific constraints (e.g., data quality or task complexity) that reduce model confidence over time. These trends highlight the importance of Bayesian methods in high-stakes alignment tasks, though real-world performance may vary based on implementation details.