## Line Graphs: F1 Score vs. #Action Across Datasets ### Overview The image contains four line graphs comparing the F1 score (y-axis) against the number of actions (#Action, x-axis) for different question-answering datasets: **HotpotQA**, **2WikiMultihopQA**, **Bamboogle**, and **MedQA**. A dashed red line labeled **ZSL** (Zero-Shot Learning) is present in all graphs, serving as a baseline. ### Components/Axes - **X-axis**: Labeled `#Action`, with values at 5, 10, 15, and 20. - **Y-axis**: Labeled `F1 (%)`, with ranges varying by dataset: - HotpotQA: 53–63% - 2WikiMultihopQA: 47–59% - Bamboogle: 56–66% - MedQA: 70–74% - **Legend**: A dashed red line labeled **ZSL** appears at the bottom of each graph. ### Detailed Analysis #### HotpotQA - **Trend**: F1 score increases steadily from ~54% at 5 actions to ~58% at 20 actions. - **ZSL Baseline**: Remains flat at ~43% across all #Action values. #### 2WikiMultihopQA - **Trend**: F1 score rises from ~54% at 5 actions to ~58% at 15 actions, then slightly declines to ~57% at 20 actions. - **ZSL Baseline**: Flat at ~47%. #### Bamboogle - **Trend**: Sharp increase from ~56% at 5 actions to ~65% at 15 actions, followed by a drop to ~63% at 20 actions. - **ZSL Baseline**: Flat at ~56%. #### MedQA - **Trend**: F1 score rises from ~70% at 5 actions to ~73% at 15 actions, then declines to ~71% at 20 actions. - **ZSL Baseline**: Flat at ~70%. ### Key Observations 1. **Performance Trends**: - All datasets show improved F1 scores with increasing #Action, except **Bamboogle** and **MedQA**, which peak at 15 actions before declining. - **ZSL** consistently underperforms compared to the main models across all datasets. 2. **Anomalies**: - **Bamboogle** exhibits the steepest rise (56% → 65%) but also the sharpest drop at 20 actions. - **MedQA** has the highest absolute F1 scores but shows a notable decline after 15 actions. ### Interpretation - **Action Efficiency**: The data suggests that increasing the number of actions generally improves performance, but diminishing returns or overfitting may occur beyond a threshold (e.g., 15 actions for Bamboogle and MedQA). - **ZSL Limitations**: The flat ZSL line indicates that zero-shot learning struggles to adapt to these datasets, highlighting the need for task-specific tuning. - **Dataset Complexity**: **MedQA**’s higher baseline F1 scores suggest it may involve simpler or more structured tasks compared to others like **HotpotQA** or **2WikiMultihopQA**. *Note: All values are approximate, derived from visual inspection of the graphs.*