## Line Chart: Pass@t vs. Consumed Tokens for Different Models and Prompting Strategies ### Overview The image is a line chart comparing the performance of ChatGPT and CodeLlama models using two different prompting strategies (EvoR and DocPrompting). The chart plots "Pass@t" (y-axis) against "Consumed tokens" (x-axis). The performance is measured by the Pass@t metric, which represents the probability of generating a correct solution within 't' attempts. The x-axis represents the number of tokens consumed during the generation process. ### Components/Axes * **X-axis:** "Consumed tokens" ranging from 4000 to 24000, with increments of 4000. * **Y-axis:** "Pass@t" ranging from 15 to 35, with increments of 5. * **Legend (Center-Right):** * Orange line with square markers: "ChatGPT - EvoR" * Green line with cross markers: "CodeLlama - EvoR" * Blue line with triangle markers: "ChatGPT - DocPrompting" * Red line with circle markers: "CodeLlama - DocPrompting" ### Detailed Analysis * **ChatGPT - EvoR (Orange):** The line starts at approximately (4000, 19) and increases rapidly until around 12000 tokens, then the increase slows down. The data points are approximately: * (4000, 19) * (8000, 30) * (12000, 34) * (16000, 35) * (20000, 36) * (24000, 36.5) * **CodeLlama - EvoR (Green):** The line starts at approximately (4000, 15) and increases steadily until around 24000 tokens. The data points are approximately: * (4000, 15) * (8000, 26.5) * (12000, 29) * (16000, 31.5) * (20000, 32.3) * (24000, 33) * **ChatGPT - DocPrompting (Blue):** The line starts at approximately (4000, 16) and increases slightly, plateauing after 12000 tokens. The data points are approximately: * (4000, 16) * (8000, 18) * (12000, 18.5) * (16000, 19) * (20000, 19.2) * (24000, 19.3) * **CodeLlama - DocPrompting (Red):** The line starts at approximately (4000, 12) and increases slightly, plateauing after 16000 tokens. The data points are approximately: * (4000, 12) * (8000, 14.2) * (12000, 15.5) * (16000, 16) * (20000, 16.3) * (24000, 16.5) ### Key Observations * ChatGPT with EvoR prompting (orange line) consistently outperforms all other configurations across the range of consumed tokens. * CodeLlama with EvoR prompting (green line) performs better than both models using DocPrompting. * Both models using DocPrompting (blue and red lines) show significantly lower Pass@t values and plateau quickly. * The performance gain from increasing consumed tokens diminishes for all configurations, especially after 16000 tokens. ### Interpretation The chart suggests that the choice of prompting strategy (EvoR vs. DocPrompting) has a significant impact on the performance of both ChatGPT and CodeLlama models. EvoR prompting leads to substantially higher Pass@t values compared to DocPrompting. ChatGPT, when combined with EvoR, achieves the highest performance, indicating a strong synergy between the model architecture and the prompting technique. The diminishing returns observed with increasing consumed tokens suggest that there is a point beyond which additional tokens do not significantly improve the probability of generating a correct solution. This could be due to the models reaching their capacity to extract relevant information or the generation process becoming less efficient with longer sequences. The DocPrompting strategy appears to be less effective in leveraging the capabilities of these models, possibly due to limitations in how the prompts are structured or the type of information they convey.

\n ## Line Chart: Performance Comparison of Language Models ### Overview This line chart compares the performance of two language models, ChatGPT and CodeLlama, under two different prompting strategies: EvoR and DocPrompting. The performance metric is "Pass@t", plotted against the number of "Consumed tokens". The chart visually demonstrates how the performance of each model changes as the number of tokens consumed increases. ### Components/Axes * **X-axis:** "Consumed tokens" - ranging from approximately 4000 to 24000. * **Y-axis:** "Pass@t" - ranging from approximately 15 to 35. * **Data Series:** * ChatGPT - EvoR (Orange) * CodeLlama - EvoR (Green) * ChatGPT - DocPrompting (Blue) * CodeLlama - DocPrompting (Red) * **Legend:** Located in the top-right corner of the chart, clearly labeling each data series with its corresponding color. ### Detailed Analysis Here's a breakdown of each data series, with approximate values extracted from the chart: * **ChatGPT - EvoR (Orange):** This line slopes sharply upward. * At 4000 tokens: approximately 19 Pass@t. * At 8000 tokens: approximately 30 Pass@t. * At 12000 tokens: approximately 32 Pass@t. * At 16000 tokens: approximately 34 Pass@t. * At 20000 tokens: approximately 35 Pass@t. * At 24000 tokens: approximately 35 Pass@t. * **CodeLlama - EvoR (Green):** This line also slopes upward, but less steeply than ChatGPT - EvoR. * At 4000 tokens: approximately 15 Pass@t. * At 8000 tokens: approximately 26 Pass@t. * At 12000 tokens: approximately 29 Pass@t. * At 16000 tokens: approximately 31 Pass@t. * At 20000 tokens: approximately 32 Pass@t. * At 24000 tokens: approximately 33 Pass@t. * **ChatGPT - DocPrompting (Blue):** This line is relatively flat, showing minimal improvement with increasing tokens. * At 4000 tokens: approximately 15 Pass@t. * At 8000 tokens: approximately 18 Pass@t. * At 12000 tokens: approximately 19 Pass@t. * At 16000 tokens: approximately 20 Pass@t. * At 20000 tokens: approximately 20 Pass@t. * At 24000 tokens: approximately 20 Pass@t. * **CodeLlama - DocPrompting (Red):** This line shows a slight upward trend, but remains the lowest performing series. * At 4000 tokens: approximately 12 Pass@t. * At 8000 tokens: approximately 14 Pass@t. * At 12000 tokens: approximately 15 Pass@t. * At 16000 tokens: approximately 15 Pass@t. * At 20000 tokens: approximately 16 Pass@t. * At 24000 tokens: approximately 16 Pass@t. ### Key Observations * ChatGPT with EvoR prompting consistently outperforms all other configurations. * The EvoR prompting strategy yields significantly better results than DocPrompting for both models. * CodeLlama generally performs lower than ChatGPT across all token ranges and prompting strategies. * The performance gains from increasing tokens diminish for ChatGPT - EvoR after approximately 16000 tokens. * The performance of ChatGPT - DocPrompting and CodeLlama - DocPrompting plateaus quickly, indicating limited benefit from increased token consumption. ### Interpretation The data suggests that the EvoR prompting strategy is far more effective at eliciting desired performance from both ChatGPT and CodeLlama than the DocPrompting strategy. ChatGPT, when combined with EvoR, demonstrates a strong positive correlation between consumed tokens and performance, up to a certain point. This implies that providing more context (through tokens) to ChatGPT with EvoR leads to improved results, but the returns diminish as the token count increases. CodeLlama, while showing improvement with EvoR and increased tokens, consistently lags behind ChatGPT. This could indicate inherent differences in the models' architectures or training data. The flat performance of both models with DocPrompting suggests that this strategy is not well-suited for the task being evaluated, or that the models require a different approach to leverage the provided context effectively. The plateauing of ChatGPT-EvoR suggests a point of diminishing returns, where further token consumption does not translate into significant performance gains. This could be due to the model reaching its capacity to effectively process and utilize the additional information. Further investigation would be needed to determine the optimal token count for maximizing performance with EvoR prompting.

## Performance Comparison Line Chart: Pass@t vs. Consumed Tokens ### Overview This image is a line chart comparing the performance of two large language models (ChatGPT and CodeLlama) using two different prompting methods (EvoR and DocPrompting). Performance is measured by the "Pass@t" metric as a function of the number of "Consumed tokens." ### Components/Axes * **Chart Type:** Line chart with markers. * **X-Axis (Horizontal):** * **Label:** "Consumed tokens" * **Scale:** Linear scale. * **Markers/Ticks:** 4000, 8000, 12000, 16000, 20000, 24000. * **Y-Axis (Vertical):** * **Label:** "Pass@t" * **Scale:** Linear scale. * **Markers/Ticks:** 15, 20, 25, 30, 35. * **Legend (Positioned center-right):** * **Orange Square (■):** "ChatGPT - EvoR" * **Green Cross (✖):** "CodeLlama - EvoR" * **Blue Triangle (▲):** "ChatGPT - DocPrompting" * **Red Circle (●):** "CodeLlama - DocPrompting" ### Detailed Analysis The chart plots four distinct data series. The trend for each is described below, followed by approximate data points extracted from the visual markers. **1. ChatGPT - EvoR (Orange line with square markers)** * **Trend:** Shows the steepest initial increase and achieves the highest overall performance. The curve rises sharply from 4000 to 12000 tokens and then continues to increase at a slower, diminishing rate. * **Approximate Data Points:** * 4000 tokens: ~19 * 8000 tokens: ~30 * 12000 tokens: ~34 * 16000 tokens: ~35.5 * 20000 tokens: ~36.5 * 24000 tokens: ~37 **2. CodeLlama - EvoR (Green line with cross markers)** * **Trend:** Follows a similar shape to ChatGPT-EvoR but consistently performs at a lower level. It also shows strong initial growth that tapers off. * **Approximate Data Points:** * 4000 tokens: ~15 * 8000 tokens: ~26.5 * 12000 tokens: ~29 * 16000 tokens: ~31.5 * 20000 tokens: ~32.5 * 24000 tokens: ~33 **3. ChatGPT - DocPrompting (Blue line with triangle markers)** * **Trend:** Shows a moderate, steady increase that plateaus early. Performance is significantly lower than both EvoR methods. * **Approximate Data Points:** * 4000 tokens: ~16.5 * 8000 tokens: ~18 * 12000 tokens: ~19 * 16000 tokens: ~19.5 * 20000 tokens: ~19.5 * 24000 tokens: ~19.5 **4. CodeLlama - DocPrompting (Red line with circle markers)** * **Trend:** Exhibits the lowest performance and the flattest growth curve. It increases slightly and then plateaus. * **Approximate Data Points:** * 4000 tokens: ~12 * 8000 tokens: ~14.5 * 12000 tokens: ~15.5 * 16000 tokens: ~16 * 20000 tokens: ~16.5 * 24000 tokens: ~16.5 ### Key Observations 1. **Method Dominance:** The "EvoR" prompting method (orange and green lines) dramatically outperforms the "DocPrompting" method (blue and red lines) for both models across all token counts. 2. **Model Comparison:** When using the same prompting method, ChatGPT consistently outperforms CodeLlama. The performance gap is larger with EvoR than with DocPrompting. 3. **Diminishing Returns:** All four curves show diminishing returns. The most significant performance gains occur between 4000 and 12000 consumed tokens. After approximately 16000 tokens, the rate of improvement slows considerably for all series. 4. **Performance Hierarchy:** The final performance ranking at 24000 tokens is clear: 1) ChatGPT-EvoR, 2) CodeLlama-EvoR, 3) ChatGPT-DocPrompting, 4) CodeLlama-DocPrompting. ### Interpretation The data suggests that the choice of prompting strategy (EvoR vs. DocPrompting) has a more significant impact on the Pass@t performance metric than the choice of base model (ChatGPT vs. CodeLlama) within this test. EvoR appears to be a far more effective technique for scaling performance with increased token consumption. The chart demonstrates a clear positive correlation between consumed tokens and Pass@t score, but with a logarithmic-like curve, indicating that simply adding more tokens yields progressively smaller benefits. This implies there is an optimal token budget range (around 12000-16000 in this context) where performance is maximized relative to resource expenditure. The consistent performance gap between models using the same method suggests underlying differences in model capability or how they interact with the specific prompting techniques. The fact that the gap between ChatGPT and CodeLlama is wider with EvoR might indicate that EvoR is better at leveraging the strengths of a more capable model like ChatGPT.

## Line Graph: Pass@t vs. Consumed Tokens ### Overview The image is a line graph comparing the performance of four AI models (ChatGPT-EvoR, CodeLlama-EvoR, ChatGPT-DocPrompting, CodeLlama-DocPrompting) across varying token consumption levels. The y-axis measures "Pass@t" (a performance metric), while the x-axis represents "Consumed tokens" (input/output token counts). Four distinct data series are plotted with unique markers and colors. ### Components/Axes - **X-axis**: "Consumed tokens" (4000–24000), incrementing by 4000. - **Y-axis**: "Pass@t" (15–35), incrementing by 5. - **Legend**: Located in the top-right corner, mapping: - Orange squares: ChatGPT - EvoR - Green crosses: CodeLlama - EvoR - Blue triangles: ChatGPT - DocPrompting - Red circles: CodeLlama - DocPrompting ### Detailed Analysis 1. **ChatGPT - EvoR (Orange Squares)**: - Starts at ~19 at 4000 tokens. - Rises sharply to ~30 at 8000 tokens. - Continues upward to ~37 at 24000 tokens. - **Trend**: Steepest slope, highest final value. 2. **CodeLlama - EvoR (Green Crosses)**: - Begins at ~15 at 4000 tokens. - Increases to ~33 at 24000 tokens. - **Trend**: Consistent upward trajectory, second-highest performance. 3. **ChatGPT - DocPrompting (Blue Triangles)**: - Starts at ~16 at 4000 tokens. - Gradually rises to ~19 at 24000 tokens. - **Trend**: Slowest growth among the four series. 4. **CodeLlama - DocPrompting (Red Circles)**: - Begins at ~12 at 4000 tokens. - Increases to ~16 at 24000 tokens. - **Trend**: Flattest slope, lowest performance. ### Key Observations - **Performance Hierarchy**: ChatGPT-EvoR > CodeLlama-EvoR > ChatGPT-DocPrompting > CodeLlama-DocPrompting. - **Token Efficiency**: All models improve performance with more tokens, but ChatGPT-EvoR gains the most. - **DocPrompting vs. EvoR**: EvoR models outperform DocPrompting counterparts by ~5–7 points at 24000 tokens. - **CodeLlama Disparity**: CodeLlama-EvoR outperforms CodeLlama-DocPrompting by ~17 points at 24000 tokens. ### Interpretation The data demonstrates that **EvoR prompting** significantly enhances performance across both models compared to **DocPrompting**. ChatGPT-EvoR achieves the highest Pass@t, suggesting superior optimization for token-intensive tasks. The steep rise in ChatGPT-EvoR’s performance between 4000–8000 tokens indicates a critical efficiency threshold. CodeLlama’s performance gap between prompting methods highlights the importance of prompt engineering for this model. The trends imply that larger token budgets disproportionately benefit EvoR-based approaches, potentially guiding resource allocation in AI deployment.