## Bar Charts: MBPP vs. Human Eval Performance ### Overview The image presents two sets of bar charts comparing the performance of a system (MBPP) against human evaluation (Human Eval) across different model sizes (0.3B to 13B). The charts display "Pass@k" values for k=1, 10, and 100, indicating the pass rate within the top k attempts. The y-axis represents the pass rate, and the x-axis represents the model size. The bars are colored differently to indicate performance relative to a baseline, with green indicating positive performance and red indicating negative performance. Error bars are included to show the uncertainty in the measurements. ### Components/Axes * **Titles:** "MBPP" (left), "Human Eval" (right) * **Y-Axis Labels (Left):** +4.5, -1.7, +3.9, -5.4, +2.2, -9.8 * **Y-Axis Labels (Right):** +1.7, -0.6, +5.0, -1.0, +7.5, -2.3 * **X-Axis Labels (Both):** 0.3B, 0.6B, 1.3B, 3B, 6.7B, 13B * **Pass@k Labels (Right):** Pass@1 (top), Pass@10 (middle), Pass@100 (bottom) * **Bar Colors:** Green (positive performance), Red (negative performance) ### Detailed Analysis **MBPP Performance** * **Pass@1:** * 0.3B: Red bar, value approximately -1.0 * 0.6B: Red bar, value approximately -1.2 * 1.3B: Green bar, value approximately 0.2 * 3B: Green bar, value approximately 1.0 * 6.7B: Green bar, value approximately 2.0 * 13B: Green bar, value approximately 3.5 * Trend: Performance generally increases with model size. * **Pass@10:** * 0.3B: Red bar, value approximately -4.0 * 0.6B: Red bar, value approximately -4.5 * 1.3B: Green bar, value approximately 0.1 * 3B: Green bar, value approximately 1.0 * 6.7B: Green bar, value approximately 2.5 * 13B: Green bar, value approximately 3.0 * Trend: Performance generally increases with model size. * **Pass@100:** * 0.3B: Red bar, value approximately -8.0 * 0.6B: Red bar, value approximately -7.0 * 1.3B: Green bar, value approximately 0.5 * 3B: Green bar, value approximately 1.5 * 6.7B: Green bar, value approximately 2.0 * 13B: Green bar, value approximately 3.0 * Trend: Performance generally increases with model size. **Human Eval Performance** * **Pass@1:** * 0.3B: Red bar, value approximately -0.5 * 0.6B: Red bar, value approximately -0.3 * 1.3B: Green bar, value approximately 0.3 * 3B: Green bar, value approximately 0.5 * 6.7B: Green bar, value approximately 1.2 * 13B: Green bar, value approximately 1.3 * Trend: Performance generally increases with model size. * **Pass@10:** * 0.3B: Red bar, value approximately -0.8 * 0.6B: Red bar, value approximately -0.7 * 1.3B: Red bar, value approximately -0.5 * 3B: Green bar, value approximately 0.3 * 6.7B: Green bar, value approximately 2.5 * 13B: Green bar, value approximately 3.5 * Trend: Performance generally increases with model size. * **Pass@100:** * 0.3B: Red bar, value approximately -2.0 * 0.6B: Red bar, value approximately -1.5 * 1.3B: Red bar, value approximately -1.0 * 3B: Green bar, value approximately 2.0 * 6.7B: Green bar, value approximately 5.0 * 13B: Green bar, value approximately 5.5 * Trend: Performance generally increases with model size. ### Key Observations * Both MBPP and Human Eval show a clear trend of increasing performance (Pass@k) as the model size increases. * Smaller models (0.3B, 0.6B, and sometimes 1.3B) tend to have negative performance (red bars), while larger models (3B, 6.7B, 13B) consistently show positive performance (green bars). * The error bars suggest that the uncertainty in the measurements is relatively small, especially for larger models. ### Interpretation The data suggests that increasing model size significantly improves the performance of both the MBPP system and in human evaluations. The negative performance of smaller models indicates that they may not be effective for the task being evaluated. The consistent positive performance of larger models suggests that they are better suited for the task. The similarity in trends between MBPP and Human Eval indicates that MBPP is a reasonable proxy for human evaluation. The Pass@k metric shows how the model performs when given k attempts to solve a problem. A higher Pass@k value indicates better performance.

## Bar Chart: Model Performance on Programming Tasks ### Overview The image presents a comparative bar chart illustrating the performance of a model (MBPP) and human evaluation on programming tasks, across different model sizes (0.3B to 13B parameters). The performance is measured using three metrics: Pass@1, Pass@10, and Pass@100, representing the probability of generating a correct solution within the first, tenth, and hundredth attempt, respectively. The chart consists of six sub-charts arranged in a 2x3 grid. ### Components/Axes * **X-axis:** Model Size (0.3B, 0.6B, 1.3B, 3B, 6.7B, 13B) - labeled in red. * **Y-axis (MBPP columns):** Performance Score (ranging approximately from -10 to +5). * **Y-axis (Human Eval columns):** Performance Score (ranging approximately from -3 to +8). * **Color Coding:** * Green: Represents positive performance gains. * Red: Represents negative performance or loss. * **Metrics:** * Pass@1: Top row of charts. * Pass@10: Middle row of charts. * Pass@100: Bottom row of charts. * **Titles:** "MBPP" (left column) and "Human Eval" (right column) are placed at the top of their respective columns. * **Labels:** Numerical values are placed above each bar, indicating the specific performance score. ### Detailed Analysis or Content Details **MBPP (Left Column)** * **Pass@1 (Top Row):** * 0.3B: Approximately -1.7, labeled "2". * 0.6B: Approximately -1.7, labeled "5". * 1.3B: Approximately 0.0, labeled "7". * 3B: Approximately +1.5, labeled "11". * 6.7B: Approximately +3.0, labeled "26". * 13B: Approximately +4.5, labeled "26". * Trend: The performance increases steadily from 0.3B to 13B. * **Pass@10 (Middle Row):** * 0.3B: Approximately -5.4, labeled "10". * 0.6B: Approximately -5.4, labeled "21". * 1.3B: Approximately -1.5, labeled "27". * 3B: Approximately +1.5, labeled "36". * 6.7B: Approximately +3.5, labeled "54". * 13B: Approximately +3.9, labeled "57". * Trend: Performance increases with model size, with a more pronounced increase from 1.3B to 3B. * **Pass@100 (Bottom Row):** * 0.3B: Approximately -9.8, labeled "30". * 0.6B: Approximately -9.8, labeled "45". * 1.3B: Approximately -2.0, labeled "51". * 3B: Approximately +0.5, labeled "60". * 6.7B: Approximately +2.0, labeled "75". * 13B: Approximately +2.2, labeled "77". * Trend: Similar to Pass@10, performance improves with model size. **Human Eval (Right Column)** * **Pass@1 (Top Row):** * 0.3B: Approximately -0.6, labeled "2". * 0.6B: Approximately -0.6, labeled "3". * 1.3B: Approximately 0.0, labeled "5". * 3B: Approximately +1.0, labeled "13". * 6.7B: Approximately +1.7, labeled "14". * 13B: Approximately +1.7, labeled "14". * Trend: Performance increases with model size, plateauing at 6.7B and 13B. * **Pass@10 (Middle Row):** * 0.3B: Approximately -1.0, labeled "5". * 0.6B: Approximately -1.0, labeled "9". * 1.3B: Approximately +0.5, labeled "13". * 3B: Approximately +2.0, labeled "17". * 6.7B: Approximately +4.0, labeled "34". * 13B: Approximately +5.0, labeled "34". * Trend: Performance increases with model size, with a significant jump between 3B and 6.7B. * **Pass@100 (Bottom Row):** * 0.3B: Approximately -2.3, labeled "11". * 0.6B: Approximately -2.3, labeled "17". * 1.3B: Approximately +0.5, labeled "24". * 3B: Approximately +3.0, labeled "30". * 6.7B: Approximately +5.5, labeled "52". * 13B: Approximately +7.5, labeled "56". * Trend: Performance increases with model size, with a substantial increase from 3B to 6.7B. ### Key Observations * The model performance (MBPP) consistently improves with increasing model size across all three metrics (Pass@1, Pass@10, Pass@100). * Human evaluation also shows a similar trend of improvement with model size, but the gains appear to plateau at larger model sizes (6.7B and 13B). * The performance gap between the model and human evaluation widens as the model size increases, particularly for Pass@10 and Pass@100. * The red bars indicate that smaller models (0.3B and 0.6B) perform poorly on all metrics, exhibiting negative performance scores. ### Interpretation The data suggests that increasing model size significantly improves performance on programming tasks, as measured by the Pass@k metrics. This is evident in both the MBPP and Human Eval columns. However, the rate of improvement appears to diminish for human evaluation at larger model sizes, indicating a potential limit to the benefits of simply scaling up the model. The widening gap between model and human performance suggests that while the model is becoming more proficient at generating correct solutions, it may still lack the nuanced understanding and problem-solving abilities of a human programmer. The negative performance scores for smaller models highlight the importance of model size for achieving reasonable performance on these tasks. The consistent trend across all metrics reinforces the conclusion that model size is a crucial factor in determining the effectiveness of these models for code generation. The use of different Pass@k metrics allows for a nuanced understanding of the model's ability to generate correct solutions with varying levels of attempts.

## Bar Charts: MBPP and Human Eval Performance by Model Size ### Overview The image displays a 2x3 grid of bar charts comparing the performance of different-sized language models on two benchmarks: **MBPP** (left column) and **Human Eval** (right column). Performance is measured using the **Pass@k** metric for k=1, 10, and 100 (rows from top to bottom). Each chart plots performance against model size (0.3B to 13B parameters). The bars are colored in two distinct groups: orange/red for the two smallest models (0.3B, 0.6B) and green for the larger models (1.3B and above). Black vertical lines on each bar represent error bars or confidence intervals. ### Components/Axes * **Charts:** Six individual bar charts arranged in two columns and three rows. * **Column Headers:** "MBPP" (left column) and "Human Eval" (right column). * **Row Labels (Right Side):** "Pass@1" (top row), "Pass@10" (middle row), "Pass@100" (bottom row). * **X-Axis (Bottom of each column):** Model sizes: `0.3B`, `0.6B`, `1.3B`, `3B`, `6.7B`, `13B`. * **Y-Axis:** Numerical scale representing the performance metric (likely percentage points or a normalized score). Each chart has its own independent scale with both positive and negative values. * **Data Labels:** Each bar has a number printed directly above it, indicating the precise value. * **Legend:** No explicit legend is present. The color grouping (orange for 0.3B/0.6B, green for 1.3B+) is consistent across all six charts. ### Detailed Analysis #### **MBPP Column (Left)** * **Pass@1 (Top-Left Chart)** * **Y-Axis Range:** -1.7 to +4.5 * **Data Points (Model Size: Value):** * 0.3B: 2 * 0.6B: 5 * 1.3B: 7 * 3B: 11 * 6.7B: 24 * 13B: 26 * **Trend:** Performance increases with model size. The growth is modest from 0.3B to 1.3B, then accelerates significantly from 3B to 13B. * **Pass@10 (Middle-Left Chart)** * **Y-Axis Range:** -5.4 to +3.9 * **Data Points (Model Size: Value):** * 0.3B: 10 * 0.6B: 21 * 1.3B: 27 * 3B: 36 * 6.7B: 54 * 13B: 57 * **Trend:** A strong, consistent upward trend. Performance more than quintuples from the smallest to the largest model. * **Pass@100 (Bottom-Left Chart)** * **Y-Axis Range:** -9.8 to +2.2 * **Data Points (Model Size: Value):** * 0.3B: 30 * 0.6B: 45 * 1.3B: 51 * 3B: 60 * 6.7B: 75 * 13B: 77 * **Trend:** Continued strong upward trend. The performance gap between 6.7B and 13B models is smaller than previous jumps, suggesting potential saturation. #### **Human Eval Column (Right)** * **Pass@1 (Top-Right Chart)** * **Y-Axis Range:** -0.6 to +1.7 * **Data Points (Model Size: Value):** * 0.3B: 2 * 0.6B: 3 * 1.3B: 5 * 3B: 13 * 6.7B: 14 * 13B: 14 * **Trend:** Performance increases with size but plateaus between 6.7B and 13B. The jump from 1.3B to 3B is the most significant. * **Pass@10 (Middle-Right Chart)** * **Y-Axis Range:** -1.0 to +5.0 * **Data Points (Model Size: Value):** * 0.3B: 5 * 0.6B: 9 * 1.3B: 13 * 3B: 17 * 6.7B: 29 * 13B: 34 * **Trend:** A clear upward trend. The rate of improvement increases notably after the 3B model. * **Pass@100 (Bottom-Right Chart)** * **Y-Axis Range:** -2.3 to +7.5 * **Data Points (Model Size: Value):** * 0.3B: 11 * 0.6B: 17 * 1.3B: 24 * 3B: 30 * 6.7B: 52 * 13B: 56 * **Trend:** Strong upward trend. A very large performance leap occurs between the 3B and 6.7B models. ### Key Observations 1. **Consistent Scaling Law:** Across both benchmarks and all `k` values, performance improves with increased model parameter count (from 0.3B to 13B). 2. **Benchmark Difficulty:** For any given model size and `k`, scores on **Human Eval** are consistently lower than on **MBPP**, suggesting Human Eval is the more challenging benchmark. 3. **Effect of `k`:** As `k` increases from 1 to 100, the absolute performance values increase dramatically for all models on both benchmarks, which is expected for the Pass@k metric. 4. **Performance Plateaus:** Evidence of diminishing returns appears in some series. For example, on Human Eval Pass@1, the 6.7B and 13B models have identical scores (14). On MBPP Pass@100, the gain from 6.7B (75) to 13B (77) is minimal. 5. **Color Grouping:** The consistent two-color scheme visually separates the "small" (0.3B, 0.6B) and "large" (1.3B+) model cohorts, emphasizing a performance threshold crossed around the 1B parameter mark. ### Interpretation These charts empirically demonstrate the scaling laws of large language models on code generation tasks. The data suggests that: * **Model size is a primary driver of capability** on standardized programming benchmarks. The relationship is not perfectly linear, with certain size transitions (e.g., 3B to 6.7B on Human Eval Pass@100) yielding outsized gains. * **The choice of metric (`k`) drastically alters the perceived performance.** A model's ability to generate a correct solution *at least once* in 100 attempts (Pass@100) is far higher than its ability to get it right on the first try (Pass@1). This highlights the importance of considering multiple evaluation metrics. * **Benchmark selection matters.** The consistent performance gap between MBPP and Human Eval indicates they test different aspects of coding ability or have different difficulty distributions. Researchers must consider which benchmark aligns with their target evaluation goals. * **The observed plateaus (e.g., Human Eval Pass@1 at 6.7B/13B) are critical.** They may indicate that for certain tasks or metrics, simply adding more parameters yields diminishing returns, and architectural innovations or data quality improvements may be needed for further progress. The error bars, while not numerically specified, suggest variability in performance, which could be due to factors like random seed or evaluation set splits.

## Bar Chart: Model Performance Comparison (MBPP vs Human Evaluation) ### Overview The image presents a comparative bar chart analyzing the performance of language models across different sizes (0.3B to 13B parameters) using two evaluation frameworks: MBPP (Math Benchmark Problems Project) and Human Evaluation. The chart uses vertical bars with positive/negative values to represent performance deviations from a baseline, with green bars indicating MBPP results and orange bars representing Human Evaluation outcomes. ### Components/Axes - **X-axis**: Model sizes (0.3B, 0.6B, 1.3B, 3B, 6.7B, 13B) - **Y-axis**: Performance metrics (Pass@1, Pass@10, Pass@100) with numerical deviations from baseline - **Legend**: - Green bars = MBPP - Orange bars = Human Evaluation - **Secondary Axis**: Numerical values above each bar (e.g., +4.5, -1.7) ### Detailed Analysis #### MBPP Section (Left) 1. **Pass@1**: - 0.3B: +4.5 - 0.6B: -1.7 - 1.3B: +3.9 - 3B: -5.4 - 6.7B: +2.2 - 13B: -9.8 2. **Pass@10**: - 0.3B: +2.5 - 0.6B: -1.7 - 1.3B: +3.9 - 3B: -5.4 - 6.7B: +2.2 - 13B: -9.8 3. **Pass@100**: - 0.3B: +4.5 - 0.6B: -1.7 - 1.3B: +3.9 - 3B: -5.4 - 6.7B: +2.2 - 13B: -9.8 #### Human Evaluation Section (Right) 1. **Pass@1**: - 0.3B: +1.7 - 0.6B: -0.6 - 1.3B: +5.0 - 3B: -1.0 - 6.7B: +7.5 - 13B: -2.3 2. **Pass@10**: - 0.3B: +1.7 - 0.6B: -0.6 - 1.3B: +5.0 - 3B: -1.0 - 6.7B: +7.5 - 13B: -2.3 3. **Pass@100**: - 0.3B: +1.7 - 0.6B: -0.6 - 1.3B: +5.0 - 3B: -1.0 - 6.7B: +7.5 - 13B: -2.3 ### Key Observations 1. **Model Size Correlation**: - MBPP shows inconsistent trends: 13B model performs worst (-9.8), while 0.3B has highest gain (+4.5) - Human Evaluation demonstrates stronger scaling: 13B model achieves +7.5 (Pass@100) vs 0.3B's +1.7 2. **Framework Differences**: - MBPP exhibits higher volatility: 6.7B model shows +2.2 (Pass@1) vs -9.8 (Pass@100) - Human Evaluation maintains more consistent performance across metrics 3. **Anomalies**: - MBPP 13B model underperforms all smaller models across all metrics - Human Evaluation 6.7B model shows strongest performance (+7.5 Pass@100) ### Interpretation The data suggests that while MBPP evaluation shows diminishing returns with larger models (potentially due to overfitting or problem-specific limitations), Human Evaluation reveals clearer benefits of model scaling. The negative values in MBPP for larger models indicate potential failure modes in complex problem-solving that aren't captured by human evaluators. The stark contrast between frameworks implies that MBPP might be measuring different aspects of model capability compared to human judgment, possibly highlighting issues with automated evaluation metrics in capturing nuanced reasoning abilities.