## Line Chart: Mean Pass Rate vs. Mean Number of Tokens Generated ### Overview The image is a line chart comparing the mean pass rate against the mean number of tokens generated for different GPT models. The chart displays five different configurations of GPT models, each represented by a distinct colored line with a shaded area indicating uncertainty. The x-axis represents the mean number of tokens generated, ranging from 0 to 10000. The y-axis represents the mean pass rate, ranging from 0.0 to 1.0. ### Components/Axes * **X-axis:** Mean number of tokens generated, with tick marks at 0, 2000, 4000, 6000, 8000, and 10000. * **Y-axis:** Mean pass rate, with tick marks at 0.0, 0.2, 0.4, 0.6, 0.8, and 1.0. * **Legend:** Located in the top-right corner, the legend identifies each line by the GPT model configuration. * Dark Blue: *M_P* = GPT-4 (no repair) * Light Green: *M_P* = GPT-4; *M_F* = GPT-4 * Gray: *M_P* = GPT-3.5 (no repair) * Brown: *M_P* = GPT-3.5; *M_F* = GPT-3.5 * Teal: *M_P* = GPT-3.5; *M_F* = GPT-4 ### Detailed Analysis * **Dark Blue Line: *M_P* = GPT-4 (no repair)** * Trend: The line starts at approximately 0.1 at 0 tokens and increases rapidly, then plateaus around 0.38 at approximately 4000 tokens, remaining relatively flat until 10000 tokens. * Data Points: (0, 0.1), (2000, 0.3), (4000, 0.38), (10000, 0.4) * **Light Green Line: *M_P* = GPT-4; *M_F* = GPT-4** * Trend: The line starts at approximately 0.15 at 0 tokens and increases rapidly, then plateaus around 0.45 at approximately 6000 tokens, remaining relatively flat until 10000 tokens. * Data Points: (0, 0.15), (2000, 0.35), (6000, 0.45), (10000, 0.5) * **Gray Line: *M_P* = GPT-3.5 (no repair)** * Trend: The line starts at approximately 0.04 at 0 tokens and increases gradually, reaching approximately 0.15 at 10000 tokens. * Data Points: (0, 0.04), (2000, 0.08), (6000, 0.12), (10000, 0.15) * **Brown Line: *M_P* = GPT-3.5; *M_F* = GPT-3.5** * Trend: The line starts at approximately 0.05 at 0 tokens and increases gradually, reaching approximately 0.18 at 10000 tokens. * Data Points: (0, 0.05), (2000, 0.1), (6000, 0.15), (10000, 0.18) * **Teal Line: *M_P* = GPT-3.5; *M_F* = GPT-4** * Trend: The line starts at approximately 0.08 at 0 tokens and increases gradually, reaching approximately 0.2 at 10000 tokens. * Data Points: (0, 0.08), (2000, 0.12), (6000, 0.17), (10000, 0.2) ### Key Observations * The GPT-4 models (*M_P* = GPT-4) consistently outperform the GPT-3.5 models in terms of mean pass rate. * Using GPT-4 for both *M_P* and *M_F* yields the highest mean pass rate. * The mean pass rate increases rapidly for all models initially, then plateaus as the number of tokens generated increases. * The shaded areas around each line indicate the uncertainty or variability in the mean pass rate. ### Interpretation The data suggests that GPT-4 models are more effective at generating passing outputs compared to GPT-3.5 models. Furthermore, using GPT-4 for both the primary model (*M_P*) and the repair model (*M_F*) results in the highest pass rate, indicating that the combination of a strong primary model and a strong repair model is beneficial. The initial rapid increase in pass rate followed by a plateau suggests that the models quickly learn to generate passing outputs, but there is a limit to how much the pass rate can be improved with additional tokens. The uncertainty, represented by the shaded areas, highlights the variability in the model's performance.

## Chart Type: Line Chart: Mean Pass Rate vs. Mean Number of Tokens Generated for Different GPT Models and Repair Strategies ### Overview This line chart illustrates the relationship between the "Mean pass rate" and the "Mean number of tokens generated" for five distinct configurations of GPT models (GPT-4 and GPT-3.5), with and without repair mechanisms. Each colored line represents a specific model configuration, and a shaded area surrounding each line indicates the associated uncertainty or confidence interval. The chart allows for a comparative analysis of model performance under varying conditions. ### Components/Axes * **X-axis Title**: "Mean number of tokens generated" * **X-axis Range**: From 0 to 10000. * **X-axis Major Tick Markers**: 0, 2000, 4000, 6000, 8000, 10000. The numerical labels are rotated counter-clockwise. * **Y-axis Title**: "Mean pass rate" * **Y-axis Range**: From 0.0 to 1.0. * **Y-axis Major Tick Markers**: 0.0, 0.2, 0.4, 0.6, 0.8, 1.0. * **Legend**: Located in the top-right quadrant of the plot area. * **Dark Blue Line**: $M_P$ = GPT-4 (no repair) * **Teal/Light Green Line**: $M_P$ = GPT-4; $M_F$ = GPT-4 * **Dark Gray Line**: $M_P$ = GPT-3.5 (no repair) * **Brown/Orange Line**: $M_P$ = GPT-3.5; $M_F$ = GPT-3.5 * **Light Blue Line**: $M_P$ = GPT-3.5; $M_F$ = GPT-4 ### Detailed Analysis All data series generally exhibit an initial rapid increase in mean pass rate as the mean number of tokens generated increases, followed by a gradual flattening, suggesting diminishing returns. Each line is accompanied by a shaded region representing its uncertainty, which tends to widen with higher token counts. 1. **Teal/Light Green Line**: $M_P$ = GPT-4; $M_F$ = GPT-4 * **Trend**: This line shows the highest performance. It starts near 0.0, rises steeply, and then continues to increase at a slower rate, consistently staying above all other lines. * **Approximate Data Points**: * At X=0: Y is approximately 0.0. * At X=2000: Y is approximately 0.35. * At X=4000: Y is approximately 0.42. * At X=6000: Y is approximately 0.45. * At X=8000: Y is approximately 0.47. * At X=10000: Y is approximately 0.49. * **Uncertainty**: The shaded area is relatively wide, particularly at higher token counts, indicating a notable range of possible pass rates. 2. **Dark Blue Line**: $M_P$ = GPT-4 (no repair) * **Trend**: This line starts around 0.1, rises sharply, and then continues to increase, but at a lower rate than the teal line. It consistently holds the second-highest performance. * **Approximate Data Points**: * At X=0: Y is approximately 0.1. * At X=2000: Y is approximately 0.3. * At X=4000: Y is approximately 0.35. * At X=6000: Y is approximately 0.37. * At X=8000: Y is approximately 0.39. * At X=10000: Y is approximately 0.41. * **Uncertainty**: The shaded area is visible and comparable in width to the teal line, suggesting similar variability. 3. **Light Blue Line**: $M_P$ = GPT-3.5; $M_F$ = GPT-4 * **Trend**: This line starts very low, rises steadily, and then flattens out. It performs significantly better than the other GPT-3.5 configurations but remains below the GPT-4 configurations. * **Approximate Data Points**: * At X=0: Y is approximately 0.02. * At X=2000: Y is approximately 0.1. * At X=4000: Y is approximately 0.15. * At X=6000: Y is approximately 0.18. * At X=8000: Y is approximately 0.21. * At X=10000: Y is approximately 0.23. * **Uncertainty**: The shaded area is present and appears to be of moderate width. 4. **Brown/Orange Line**: $M_P$ = GPT-3.5; $M_F$ = GPT-3.5 * **Trend**: This line starts very low, rises gradually, and then flattens out. It performs slightly better than the dark gray line but considerably worse than the light blue line. * **Approximate Data Points**: * At X=0: Y is approximately 0.02. * At X=2000: Y is approximately 0.07. * At X=4000: Y is approximately 0.1. * At X=6000: Y is approximately 0.12. * At X=8000: Y is approximately 0.14. * At X=10000: Y is approximately 0.16. * **Uncertainty**: The shaded area is visible and appears to be of moderate width. 5. **Dark Gray Line**: $M_P$ = GPT-3.5 (no repair) * **Trend**: This line starts very low, rises gradually, and then flattens out. It consistently shows the lowest performance across the entire range. * **Approximate Data Points**: * At X=0: Y is approximately 0.02. * At X=2000: Y is approximately 0.05. * At X=4000: Y is approximately 0.08. * At X=6000: Y is approximately 0.1. * At X=8000: Y is approximately 0.12. * At X=10000: Y is approximately 0.14. * **Uncertainty**: The shaded area is present and appears to be of moderate width, similar to the brown line. ### Key Observations * **GPT-4 Dominance**: Configurations using GPT-4 as the primary model ($M_P$) consistently achieve significantly higher mean pass rates compared to those using GPT-3.5 as the primary model. * **Repair Mechanism Effectiveness**: * For GPT-4, employing GPT-4 for repair ($M_F$ = GPT-4) results in the highest pass rate, outperforming GPT-4 with no repair. * For GPT-3.5, using GPT-3.5 for repair ($M_F$ = GPT-3.5) provides a marginal improvement over GPT-3.5 with no repair. * **Cross-Model Repair Impact**: The most substantial improvement for a GPT-3.5 primary model is observed when GPT-4 is used for repair ($M_P$ = GPT-3.5; $M_F$ = GPT-4). This configuration achieves a pass rate roughly double that of GPT-3.5 with no repair or GPT-3.5 with GPT-3.5 repair, and approaches the performance of GPT-4 with no repair. * **Performance Ceiling**: All models show a rapid increase in pass rate at lower token counts (up to ~4000 tokens), after which the rate of improvement diminishes, suggesting a plateau in performance gains beyond a certain number of generated tokens. * **Uncertainty Growth**: The confidence intervals (shaded areas) generally widen as the number of tokens generated increases, indicating greater variability in pass rates at higher token counts. ### Interpretation This chart provides compelling evidence regarding the capabilities of different large language models (GPT-4 vs. GPT-3.5) and the strategic benefits of incorporating a "repair" mechanism, particularly when leveraging a more powerful model for repair. 1. **Inherent Model Strength**: GPT-4 demonstrates superior inherent capability in achieving higher pass rates, even without any explicit repair process. This suggests that GPT-4's initial generations are of higher quality or more robust. 2. **Synergy of Generation and Repair**: The best performance is achieved when GPT-4 is used for both primary generation ($M_P$) and repair ($M_F$). This indicates that even highly capable models can benefit from an iterative refinement process, where the same strong model can identify and correct its own potential flaws. 3. **Upskilling with Repair**: A key finding is the significant performance boost for GPT-3.5 when paired with GPT-4 for repair. This "cross-model repair" strategy allows a less capable primary generator (GPT-3.5) to achieve pass rates comparable to, or even exceeding, a stronger model (GPT-4) operating without repair. This implies that a powerful repair model can effectively compensate for the limitations of a weaker generation model, making it a valuable strategy for improving output quality without solely relying on the most advanced primary generator. 4. **Efficiency Considerations**: The flattening of the curves suggests that there is an optimal range for the number of tokens generated. Beyond approximately 6000-8000 tokens, the marginal gains in pass rate become minimal. This has practical implications for resource allocation, as generating an excessive number of tokens might not yield proportional improvements in pass rate, leading to inefficient computation. 5. **Strategic Model Deployment**: The data suggests a tiered approach to model deployment. For maximum performance, GPT-4 with GPT-4 repair is ideal. If cost or latency constraints limit the use of GPT-4 for primary generation, a hybrid approach of GPT-3.5 for generation and GPT-4 for repair offers a highly effective alternative, significantly outperforming GPT-3.5 alone. This strategy allows for a balance between performance and resource utilization.

\n ## Line Chart: Pass Rate vs. Tokens Generated for Different GPT Models ### Overview This line chart depicts the relationship between the mean number of tokens generated and the mean pass rate for several configurations of GPT models. The chart compares the performance of GPT-4 and GPT-3.5, both with and without a "repair" mechanism (denoted by *M_P* and *M_F*). Shaded areas around each line represent uncertainty, likely standard deviation or confidence intervals. ### Components/Axes * **X-axis:** "Mean number of tokens generated" - Scale ranges from 0 to 10000, with tick marks at 0, 2000, 4000, 6000, 8000, and 10000. * **Y-axis:** "Mean pass rate" - Scale ranges from 0.0 to 1.0, with tick marks at 0.0, 0.2, 0.4, 0.6, 0.8, and 1.0. * **Legend:** Located in the top-right corner of the chart. Contains the following labels and corresponding colors: * Black: *M_P* = GPT-4 (no repair) * Gray: *M_P* = GPT-4; *M_F* = GPT-4 * Light Blue: *M_P* = GPT-3.5 (no repair) * Orange: *M_P* = GPT-3.5; *M_F* = GPT-3.5 * Blue: *M_P* = GPT-3.5; *M_F* = GPT-4 ### Detailed Analysis Here's a breakdown of each line's trend and approximate data points, verified against the legend colors: * **GPT-4 (no repair) - Black Line:** The line starts at approximately (0, 0.05) and slopes upward, leveling off around (10000, 0.42). The shaded area indicates uncertainty, ranging from approximately 0.35 to 0.48 at 10000 tokens. * **GPT-4; GPT-4 - Gray Line:** This line begins at approximately (0, 0.03) and exhibits a similar upward trend to GPT-4 (no repair), but remains consistently lower. It plateaus around (10000, 0.38), with a shaded uncertainty range of approximately 0.32 to 0.44. * **GPT-3.5 (no repair) - Light Blue Line:** Starting at approximately (0, 0.02), this line shows a moderate upward slope, but is significantly lower than the GPT-4 lines. It reaches approximately (10000, 0.22), with a shaded uncertainty range of approximately 0.18 to 0.26. * **GPT-3.5; GPT-3.5 - Orange Line:** This line begins at approximately (0, 0.01) and increases slowly, remaining the lowest of all lines. It plateaus around (10000, 0.18), with a shaded uncertainty range of approximately 0.14 to 0.22. * **GPT-3.5; GPT-4 - Blue Line:** Starting at approximately (0, 0.04), this line shows a moderate upward slope, and is higher than the GPT-3.5; GPT-3.5 line, but lower than the GPT-4 lines. It plateaus around (10000, 0.35), with a shaded uncertainty range of approximately 0.30 to 0.40. ### Key Observations * GPT-4 consistently outperforms GPT-3.5 across all token generation ranges. * The "repair" mechanism (*M_F*) appears to have a varying impact depending on the base model. When applied to GPT-3.5 with GPT-4 (*M_P* = GPT-3.5; *M_F* = GPT-4), it significantly improves the pass rate compared to GPT-3.5 alone. * The uncertainty bands suggest that the pass rates are not highly predictable, and there is considerable variation in performance. * The pass rate appears to plateau as the number of tokens generated increases, indicating diminishing returns. ### Interpretation The data suggests that GPT-4 is a more reliable model for generating successful outputs (as measured by the pass rate) than GPT-3.5. The "repair" mechanism, represented by *M_F*, seems to be most effective when combined with GPT-3.5 and GPT-4, suggesting a synergistic effect. The plateauing of the pass rate with increasing tokens indicates that there's a limit to how much improvement can be achieved by simply generating more text. The uncertainty bands highlight the inherent stochasticity of these models; even with the same input and settings, the output quality can vary. The notation *M_P* and *M_F* likely refer to a "prompting" and "fixing" or "feedback" stage in the generation process. *M_P* likely represents the initial model used for prompting, while *M_F* represents a second model used to refine or "repair" the output. The results suggest that using a more powerful model (GPT-4) for the repair stage can significantly improve the overall pass rate, even when starting with a less powerful model (GPT-3.5).

## Line Chart: Mean Pass Rate vs. Mean Number of Tokens Generated ### Overview The image is a line chart displaying the relationship between the mean number of tokens generated (x-axis) and the mean pass rate (y-axis) for five different model configurations. The chart compares the performance of GPT-4 and GPT-3.5 models, both with and without a "repair" mechanism. Each line is accompanied by a shaded region representing a confidence interval or variability. ### Components/Axes * **Chart Type:** Line chart with shaded confidence bands. * **X-Axis:** * **Label:** "Mean number of tokens generated" * **Scale:** Linear scale from 0 to 10,000. * **Major Ticks:** 0, 2000, 4000, 6000, 8000, 10000. * **Y-Axis:** * **Label:** "Mean pass rate" * **Scale:** Linear scale from 0.0 to 1.0. * **Major Ticks:** 0.0, 0.2, 0.4, 0.6, 0.8, 1.0. * **Legend:** Located in the top-right quadrant of the chart area. It contains five entries, each with a colored line sample and a text label. 1. **Dark Blue Line:** `M_P = GPT-4 (no repair)` 2. **Teal Line:** `M_P = GPT-4; M_F = GPT-4` 3. **Gray Line:** `M_P = GPT-3.5 (no repair)` 4. **Orange Line:** `M_P = GPT-3.5; M_F = GPT-3.5` 5. **Light Blue Line:** `M_P = GPT-3.5; M_F = GPT-4` * **Grid:** A light gray grid is present, aligned with the major ticks on both axes. ### Detailed Analysis The chart plots five data series. All series show a logarithmic-like growth trend: a rapid initial increase in mean pass rate as the mean number of tokens generated increases, followed by a gradual flattening (diminishing returns). **Trend Verification & Data Point Extraction (Approximate Values):** 1. **`M_P = GPT-4 (no repair)` (Dark Blue Line):** * **Trend:** Starts low, rises steeply until ~2000 tokens, then continues to rise at a decreasing rate. * **Approximate Points:** At 0 tokens: ~0.10. At 2000 tokens: ~0.32. At 10000 tokens: ~0.41. 2. **`M_P = GPT-4; M_F = GPT-4` (Teal Line):** * **Trend:** Follows a similar shape to the dark blue line but is consistently higher. This is the top-performing series. * **Approximate Points:** At 0 tokens: ~0.12. At 2000 tokens: ~0.35. At 10000 tokens: ~0.49. 3. **`M_P = GPT-3.5 (no repair)` (Gray Line):** * **Trend:** The lowest-performing series. Shows a very shallow, almost linear increase. * **Approximate Points:** At 0 tokens: ~0.03. At 2000 tokens: ~0.08. At 10000 tokens: ~0.15. 4. **`M_P = GPT-3.5; M_F = GPT-3.5` (Orange Line):** * **Trend:** Performs better than the GPT-3.5 (no repair) baseline but worse than all GPT-4 configurations. * **Approximate Points:** At 0 tokens: ~0.03. At 2000 tokens: ~0.09. At 10000 tokens: ~0.18. 5. **`M_P = GPT-3.5; M_F = GPT-4` (Light Blue Line):** * **Trend:** The best-performing configuration using GPT-3.5 as the primary model (`M_P`). It shows a more pronounced upward curve than the other GPT-3.5 lines. * **Approximate Points:** At 0 tokens: ~0.03. At 2000 tokens: ~0.11. At 10000 tokens: ~0.23. **Spatial Grounding & Confidence Intervals:** * The shaded bands around each line indicate uncertainty. The bands for the top two (GPT-4) lines are wider, suggesting greater variance in their performance. * The legend is positioned in the top-right, overlapping the upper portion of the chart but not obscuring critical data trends. ### Key Observations 1. **Model Hierarchy:** There is a clear performance hierarchy: GPT-4 based configurations significantly outperform all GPT-3.5 based configurations across the entire range of token counts. 2. **Impact of Repair (`M_F`):** For both GPT-4 and GPT-3.5, adding a repair model (`M_F`) improves the mean pass rate compared to the "no repair" baseline. The improvement is more dramatic for GPT-4. 3. **Cross-Model Repair:** Using a stronger model for repair (`M_F = GPT-4`) provides a notable boost to the weaker primary model (`M_P = GPT-3.5`), lifting its performance curve above the `M_P = GPT-3.5; M_F = GPT-3.5` configuration. 4. **Diminishing Returns:** All curves exhibit diminishing returns; the gain in pass rate per additional token generated decreases as the total token count increases. The curves begin to plateau after approximately 4000-6000 tokens. ### Interpretation This chart demonstrates the efficacy of a "repair" or refinement step (`M_F`) in improving the pass rate of generated code or text (implied by "pass rate" and "tokens generated"). The data suggests two key insights: 1. **Base Model Capability is Paramount:** The primary model (`M_P`) sets the fundamental performance ceiling. No amount of repair with GPT-3.5 can bring a GPT-3.5-based system to the level of a GPT-4 system, even one without repair. 2. **Repair is a Force Multiplier:** The repair mechanism acts as a multiplier on the base model's capability. It is most effective when applied to a strong base model (GPT-4 + GPT-4 repair yields the highest absolute performance). However, it can also be used strategically to elevate a weaker model's output, as seen by `M_P = GPT-3.5; M_F = GPT-4` outperforming `M_P = GPT-3.5; M_F = GPT-3.5`. The chart implies that for tasks where generating more tokens (e.g., through iterative refinement or longer solutions) is acceptable, investing in both a strong base model and a strong repair model yields the best results. The plateauing curves also suggest there may be an optimal token budget beyond which further generation provides minimal quality improvement.

## Heatmap: Mean Pass Rate vs. Mean Number of Tokens Generated ### Overview The heatmap illustrates the mean pass rate of different models as the mean number of tokens generated increases. The models compared are GPT-4, GPT-3.5, and their variants with and without repair mechanisms. ### Components/Axes - **X-axis**: Mean number of tokens generated, ranging from 0 to 10,000. - **Y-axis**: Mean pass rate, ranging from 0.0 to 1.0. - **Legend**: - **M_p**: GPT-4 (no repair) - **M_p; M_f**: GPT-4; GPT-4 - **M_p**: GPT-3.5 (no repair) - **M_p; M_f**: GPT-3.5; GPT-3.5 - **M_p**: GPT-3.5; GPT-4 ### Detailed Analysis or ### Content Details The heatmap shows that the mean pass rate generally increases with the mean number of tokens generated. However, the rate of increase varies between the models. GPT-4 (no repair) has the highest mean pass rate, followed by GPT-4 with repair (M_p; M_f). GPT-3.5 (no repair) and GPT-3.5 with repair (M_p; M_f) have lower pass rates compared to GPT-4. The pass rate for GPT-3.5 with repair (M_p; M_f) is slightly higher than GPT-3.5 (no repair) across all token counts. ### Key Observations - GPT-4 (no repair) consistently has the highest mean pass rate. - GPT-4 with repair (M_p; M_f) shows a slight improvement in pass rate compared to GPT-4 (no repair). - GPT-3.5 (no repair) and GPT-3.5 with repair (M_p; M_f) have lower pass rates. - The pass rate for GPT-3.5 with repair (M_p; M_f) is marginally higher than GPT-3.5 (no repair). ### Interpretation The data suggests that the mean pass rate of language models generally improves with the number of tokens generated. The models with repair mechanisms (M_p; M_f) tend to have higher pass rates compared to their variants without repair (M_p). This could indicate that repair mechanisms help in improving the quality and coherence of the generated text. However, the improvement is relatively small, suggesting that the models are already quite effective in generating text with a high pass rate.

## Line Graph: Mean Pass Rate vs. Mean Number of Tokens Generated ### Overview The graph compares the performance of different GPT model configurations (GPT-4 and GPT-3.5) across two scenarios: "no repair" and "with repair" (denoted as _M_P_ and _M_F_). Performance is measured as "Mean pass rate" against the "Mean number of tokens generated" (x-axis). Five data series are plotted, with shaded regions indicating uncertainty. ### Components/Axes - **X-axis**: "Mean number of tokens generated" (0 to 10,000, logarithmic scale). - **Y-axis**: "Mean pass rate" (0.0 to 1.0, linear scale). - **Legend**: Located in the top-right corner, with five entries: 1. Dark blue: _M_P_ = GPT-4 (no repair) 2. Teal: _M_P_ = GPT-4; _M_F_ = GPT-4 (repair) 3. Gray: _M_P_ = GPT-3.5 (no repair) 4. Orange: _M_P_ = GPT-3.5; _M_F_ = GPT-3.5 (repair) 5. Light blue: _M_P_ = GPT-3.5; _M_F_ = GPT-4 (repair) ### Detailed Analysis 1. **Dark Blue Line (GPT-4, no repair)**: - Starts at ~0.1 pass rate at 1,000 tokens. - Rises steadily to ~0.4 pass rate at 10,000 tokens. - Shaded region widens slightly, indicating moderate uncertainty. 2. **Teal Line (GPT-4 with repair)**: - Begins at ~0.05 pass rate at 1,000 tokens. - Reaches ~0.5 pass rate at 10,000 tokens. - Shaded region is the widest, suggesting higher variability. 3. **Gray Line (GPT-3.5, no repair)**: - Starts at ~0.02 pass rate at 1,000 tokens. - Ends at ~0.15 pass rate at 10,000 tokens. - Shaded region is narrow, indicating low uncertainty. 4. **Orange Line (GPT-3.5 with repair)**: - Begins at ~0.01 pass rate at 1,000 tokens. - Ends at ~0.18 pass rate at 10,000 tokens. - Shaded region is moderately wide. 5. **Light Blue Line (GPT-3.5 with GPT-4 repair)**: - Starts at ~0.03 pass rate at 1,000 tokens. - Ends at ~0.22 pass rate at 10,000 tokens. - Shaded region is the narrowest, indicating high confidence. ### Key Observations - **GPT-4 superiority**: All GPT-4 configurations outperform GPT-3.5 variants, especially at higher token counts. - **Repair impact**: Repair mechanisms improve pass rates for both models, with GPT-4 showing the largest gains. - **Uncertainty patterns**: GPT-4 models exhibit wider shaded regions, suggesting greater variability in performance across trials. - **Efficiency trade-off**: GPT-3.5 with GPT-4 repair achieves ~20% pass rate at 10,000 tokens but requires more tokens than GPT-4 alone. ### Interpretation The data demonstrates that GPT-4 models consistently achieve higher pass rates than GPT-3.5, with repair mechanisms amplifying performance gains. The shaded regions imply that GPT-4's results are less predictable, possibly due to architectural complexity or training data differences. The light blue line (hybrid repair) suggests that combining GPT-3.5 with GPT-4 repair offers a cost-effective middle ground, though it underperforms pure GPT-4 configurations. These trends highlight the trade-offs between model size, repair strategies, and computational efficiency in NLP systems.