\n ## Line Chart: Accuracy vs. Decoding Paths ### Overview This line chart illustrates the relationship between the number of top-k tokens used for decoding paths and the resulting accuracy for different decoding strategies. The chart compares the performance of "CoT-decoding (max path)", "CoT-decoding (agg path)", "Few-shot CoT", "Zero-shot CoT", and "Greedy Decode". ### Components/Axes * **X-axis:** "Top-k tokens for decoding paths". Scale ranges from approximately 0 to 40, with markers at 0, 10, 20, 30, and 40. * **Y-axis:** "Accuracy". Scale ranges from approximately 0 to 80, with markers at 0, 20, 40, 60, and 80. * **Lines:** * Blue line: "CoT-decoding (max path)" * Orange line: "CoT-decoding (agg path)" * Purple dashed line: "Few-shot CoT" * Green dashed line: "Zero-shot CoT" * Red dashed line: "Greedy Decode" * **Legend:** Located in the top-right quadrant of the chart. ### Detailed Analysis * **CoT-decoding (max path) - Blue Line:** The line starts at approximately 30 accuracy at k=0, rises to a peak of approximately 65 accuracy around k=15, and then fluctuates between 62 and 65 accuracy for the remainder of the range. * **CoT-decoding (agg path) - Orange Line:** This line exhibits a strong upward trend. It begins at approximately 30 accuracy at k=0, steadily increases to approximately 78 accuracy at k=40. * **Few-shot CoT - Purple Dashed Line:** This is a horizontal line at approximately 80 accuracy across the entire range of k values. * **Zero-shot CoT - Green Dashed Line:** This is a horizontal line at approximately 75 accuracy across the entire range of k values. * **Greedy Decode - Red Dashed Line:** This is a horizontal line at approximately 30 accuracy across the entire range of k values. ### Key Observations * "CoT-decoding (agg path)" consistently outperforms "CoT-decoding (max path)" across all values of k. * "Few-shot CoT" achieves the highest accuracy, followed by "Zero-shot CoT". * "Greedy Decode" exhibits the lowest accuracy and remains constant regardless of the number of decoding paths. * The accuracy of "CoT-decoding (max path)" plateaus after k=15, suggesting diminishing returns from increasing the number of decoding paths beyond that point. * The "CoT-decoding (agg path)" shows a clear positive correlation between the number of top-k tokens and accuracy. ### Interpretation The data suggests that using aggregated decoding paths ("CoT-decoding (agg path)") significantly improves accuracy compared to using the maximum path ("CoT-decoding (max path)"). The performance of both CoT-decoding methods is substantially lower than that of "Few-shot CoT" and "Zero-shot CoT", indicating that providing examples (few-shot) or leveraging the model's inherent reasoning capabilities (zero-shot) are more effective strategies. "Greedy Decode" is the least effective approach, highlighting the importance of exploring multiple decoding paths. The plateau in "CoT-decoding (max path)" suggests that there's a limit to the benefit of exploring more paths when only selecting the best one. The consistent improvement in "CoT-decoding (agg path)" indicates that combining information from multiple paths leads to more robust and accurate results. The horizontal lines for the CoT methods suggest that their performance is independent of the number of decoding paths considered, as they are already leveraging a form of reasoning or knowledge.