\n ## Bar Charts: LLM Call Performance Across Datasets ### Overview The image presents three bar charts, each representing the distribution of the number of Large Language Model (LLM) calls required to answer questions from different datasets: CWQ, WebQSP, and GrailQA. The y-axis represents the percentage of questions answered within a given number of LLM calls, while the x-axis categorizes the number of LLM calls into bins. ### Components/Axes * **X-axis Title:** "Number of LLM calls" * **Y-axis Title:** "Percentage %" * **Datasets (Chart Titles):** CWQ, WebQSP, GrailQA * **X-axis Categories:** (0, 3], (3, 6], (6, 9], (9, 12], (12, 15], (15, 18], (18, 21], 21+ * **Color Scheme:** * CWQ: Orange * WebQSP: Yellow-Orange * GrailQA: Blue ### Detailed Analysis **CWQ (Left Chart):** The distribution peaks in the (3, 6] call range. The trend is a decreasing frequency as the number of LLM calls increases. * (0, 3]: Approximately 8% * (3, 6]: Approximately 42% * (6, 9]: Approximately 25% * (9, 12]: Approximately 12% * (12, 15]: Approximately 6% * (15, 18]: Approximately 4% * (18, 21]: Approximately 2% * 21+: Approximately 1% **WebQSP (Center Chart):** The distribution peaks in the (6, 9] call range. The trend is a decreasing frequency as the number of LLM calls increases. * (0, 3]: Approximately 10% * (3, 6]: Approximately 25% * (6, 9]: Approximately 45% * (9, 12]: Approximately 10% * (12, 15]: Approximately 5% * (15, 18]: Approximately 3% * (18, 21]: Approximately 1% * 21+: Approximately 1% **GrailQA (Right Chart):** The distribution peaks in the (9, 12] call range. The trend is a decreasing frequency as the number of LLM calls increases. * (0, 3]: Approximately 5% * (3, 6]: Approximately 15% * (6, 9]: Approximately 20% * (9, 12]: Approximately 70% * (12, 15]: Approximately 5% * (15, 18]: Approximately 2% * (18, 21]: Approximately 1% * 21+: Approximately 2% ### Key Observations * CWQ generally requires fewer LLM calls than WebQSP and GrailQA. * GrailQA has a very strong peak at 9-12 LLM calls, indicating that most questions can be answered within this range. * WebQSP shows a broader distribution, suggesting more variability in the complexity of questions. * The percentage of questions requiring 21+ LLM calls is consistently low across all datasets. ### Interpretation The data suggests that the difficulty of answering questions varies significantly across the three datasets. GrailQA appears to be the most amenable to LLM-based question answering, with a large proportion of questions resolved within a relatively small number of calls. CWQ is also relatively efficient, while WebQSP requires a more extensive search process, as evidenced by its broader distribution. The low percentage of questions requiring a very large number of LLM calls (21+) across all datasets indicates that the LLM is generally able to converge on an answer within a reasonable number of iterations. This could be due to the quality of the LLM, the nature of the datasets, or a combination of both. The differences in distributions likely reflect the inherent complexity and characteristics of each dataset – for example, the type of questions asked, the depth of knowledge required, and the ambiguity of the language used.