\n ## Bar Chart: Accuracy Comparison of Models ### Overview This bar chart compares the accuracy of different model configurations on two datasets: ProofWriter and LogicNLI. The chart displays accuracy percentages for a baseline model ("Ours") and variations where specific components are removed ("w/o Decomposer", "w/o Search Router", "w/o Resolver"). ### Components/Axes * **X-axis:** Represents the datasets: "ProofWriter" and "LogicNLI". * **Y-axis:** Represents "Accuracy (%)", ranging from approximately 30% to 90%. * **Legend:** * Dark Red: "Ours" (Baseline model) * Medium Red: "w/o Decomposer" (Model without the Decomposer component) * Light Red: "w/o Search Router" (Model without the Search Router component) * Pale Red: "w/o Resolver" (Model without the Resolver component) ### Detailed Analysis The chart consists of two sets of four bars, one for each dataset. **ProofWriter Dataset:** * "Ours" (Dark Red): The bar reaches approximately 88.5%. * "w/o Decomposer" (Medium Red): The bar reaches approximately 62.0%. * "w/o Search Router" (Light Red): The bar reaches approximately 47.1%. * "w/o Resolver" (Pale Red): The bar reaches approximately 37.7%. **LogicNLI Dataset:** * "Ours" (Dark Red): The bar reaches approximately 70.7%. * "w/o Decomposer" (Medium Red): The bar reaches approximately 42.8%. * "w/o Search Router" (Light Red): The bar reaches approximately 39.1%. * "w/o Resolver" (Pale Red): The bar reaches approximately 57.5%. ### Key Observations * The "Ours" model consistently outperforms all variations across both datasets. * Removing the "Decomposer" component results in a significant accuracy drop for both datasets, but the drop is more pronounced in ProofWriter. * Removing the "Search Router" and "Resolver" components leads to substantial accuracy reductions, particularly in ProofWriter. * The "w/o Resolver" model performs relatively better on LogicNLI compared to ProofWriter, while the other variations show a more consistent performance drop across datasets. ### Interpretation The data suggests that all three components – Decomposer, Search Router, and Resolver – contribute significantly to the overall accuracy of the model. The Decomposer appears to be the most critical component, as its removal leads to the largest performance decrease. The difference in performance between the datasets suggests that the importance of each component may vary depending on the characteristics of the data. The ProofWriter dataset seems to be more sensitive to the removal of the Search Router and Resolver components than the LogicNLI dataset. This could indicate that ProofWriter requires more sophisticated search and resolution mechanisms. The chart demonstrates the importance of each component in the model architecture and provides insights into their relative contributions to performance. The consistent outperformance of the "Ours" model highlights the effectiveness of the complete architecture.