## Bar Chart: CoC (try Python except LM) ### Overview The image is a bar chart displaying the delta (Δ) with respect to the average human rater, expressed as a percentage. The x-axis is not explicitly labeled, but it represents different categories or models. The bars are colored in a gradient from orange to blue, with orange bars representing negative deltas and blue bars representing positive deltas. The chart title is "CoC (try Python except LM)". ### Components/Axes * **Title:** CoC (try Python except LM) * **Y-axis Label:** Δ w.r.t. average human rater (%) * **Y-axis Scale:** -100, -50, 0, 50, 100 * **X-axis:** Implicit categories represented by the bars. * **Bar Colors:** Gradient from orange to blue. ### Detailed Analysis The chart shows a clear trend: the bars start with negative values (orange) and gradually increase to positive values (blue). * **Orange Bars (Negative Deltas):** * The first bar has a value of approximately -35%. * The second bar has a value of approximately -30%. * The third bar has a value of approximately -15%. * The fourth bar has a value of approximately -10%. * The fifth bar has a value of approximately -5%. * **Bars Near Zero:** * The sixth bar has a value of approximately -2%. * The seventh bar has a value of approximately 1%. * **Blue Bars (Positive Deltas):** * The eighth bar has a value of approximately 5%. * The ninth bar has a value of approximately 8%. * The tenth bar has a value of approximately 10%. * The eleventh bar has a value of approximately 12%. * The twelfth bar has a value of approximately 15%. * The thirteenth bar has a value of approximately 20%. * The fourteenth bar has a value of approximately 25%. * The fifteenth bar has a value of approximately 35%. * The sixteenth bar has a value of approximately 40%. * The seventeenth bar has a value of approximately 50%. * The eighteenth bar has a value of approximately 95%. ### Key Observations * There is a clear upward trend from negative to positive deltas. * The last bar (rightmost) shows a significantly higher positive delta compared to the other bars. * The bars transition from orange to blue, indicating a shift from negative to positive differences relative to the average human rater. ### Interpretation The chart compares different models or categories (represented by the bars) against the average human rater. The y-axis represents the difference in performance between each model and the human rater. The negative values (orange bars) indicate that the model performed worse than the average human rater, while the positive values (blue bars) indicate that the model performed better. The upward trend suggests that the models generally improve as you move from left to right on the x-axis. The last bar, with a significantly higher positive delta, indicates that one particular model performs substantially better than the average human rater. The title "CoC (try Python except LM)" suggests that the models being compared are related to Chain of Command (CoC) and that Python was used, except for Language Models (LM).

\n ## Bar Chart: CoC (try Python except LM) ### Overview The image presents a bar chart comparing the performance of a system ("CoC") against an average human rater, specifically when trying Python code excluding Large Language Models (LM). The y-axis represents the difference (Δ) in performance relative to the average human rater, expressed as a percentage. The x-axis represents a series of trials or steps, with each bar representing the performance at that step. ### Components/Axes * **Title:** "CoC (try Python except LM)" - positioned at the top-center of the chart. * **Y-axis Label:** "Δ w.r.t. average human rater (%)" - positioned on the left side of the chart. The scale ranges from approximately -100% to 100%. * **X-axis:** Implied sequential steps or trials. No explicit labels are present on the x-axis. * **Color Scheme:** Two distinct colors are used: orange for negative differences and blue for positive differences. ### Detailed Analysis The chart displays a series of bars representing the performance difference. The initial bars (approximately the first 10) are orange, indicating performance below the average human rater. These bars range from approximately -10% to 0%. After this initial segment, the bars transition to blue, indicating performance above the average human rater. The blue bars gradually increase in height, starting around 0% and reaching approximately 80-90% for the final bars. Here's a breakdown of approximate values, reading from left to right: * Bar 1: -10% * Bar 2: -5% * Bar 3: ~-3% * Bar 4: ~-1% * Bar 5: ~0% * Bar 6: ~0% * Bar 7: ~5% * Bar 8: ~10% * Bar 9: ~15% * Bar 10: ~20% * Bar 11: ~25% * Bar 12: ~35% * Bar 13: ~45% * Bar 14: ~55% * Bar 15: ~70% * Bar 16: ~85% The trend is a clear shift from underperforming (negative values) to significantly outperforming (positive values) the average human rater as the number of trials increases. ### Key Observations * The system initially performs worse than the average human rater. * There is a distinct transition point where the system's performance surpasses that of the human rater. * The performance improvement is not linear; it accelerates towards the end of the trials. * The final performance is substantially higher than the human rater, reaching approximately 85-90%. ### Interpretation The data suggests that the "CoC" system, when attempting Python code without the aid of a Large Language Model, experiences a learning curve. Initially, it struggles to match human performance. However, with each trial, it improves, eventually exceeding human capabilities by a significant margin. This could indicate that the system benefits from iterative refinement or learning from its mistakes. The fact that the improvement is not linear suggests that there may be a critical mass of experience or a specific point at which the system unlocks a more effective strategy. The exclusion of Large Language Models is important to note, as it suggests the observed performance is due to the system's inherent capabilities rather than reliance on pre-trained models. The chart demonstrates a clear positive trend in performance over time, highlighting the potential of the system to become a valuable tool for code generation or assistance.

## Bar Chart: CoC (try Python except LM) ### Overview The image is a vertical bar chart titled "CoC (try Python except LM)". It displays the performance difference (delta) of various models or methods relative to an average human rater, expressed as a percentage. The chart shows a clear progression from negative to positive performance values, with bars ordered from left (lowest) to right (highest). ### Components/Axes * **Title:** "CoC (try Python except LM)" (located at the top center). * **Y-Axis:** * **Label:** "Δ w.r.t. average human rater (%)". This indicates the metric is the percentage change or difference compared to a baseline human rater average. * **Scale:** Linear scale ranging from -100 to 100. * **Major Tick Marks:** At -100, -50, 0, 50, and 100. * **X-Axis:** No explicit label or category names are provided. The axis contains 20 discrete bars, each representing a distinct item (likely a model, method, or experimental condition). * **Legend:** No legend is present in the image. Categories are distinguished solely by bar color and position. * **Bar Colors:** The bars follow a color gradient: * **Leftmost bars (positions 1-4):** Orange to light orange. * **Middle bars (positions 5-10):** Transitioning through brownish/mauve to grayish-purple. * **Rightmost bars (positions 11-20):** Progressing from light blue to dark blue. ### Detailed Analysis The chart contains 20 bars. Their approximate values, read from left to right, are as follows. Values are estimated based on the y-axis scale. | Bar Position (Left to Right) | Approximate Value (Δ %) | Color Description | Visual Trend | | :--- | :--- | :--- | :--- | | 1 | -35% | Orange | Starts the series at the lowest point. | | 2 | -30% | Light Orange | Slightly higher than bar 1. | | 3 | -20% | Light Orange | Continues upward trend. | | 4 | -15% | Light Orange | | | 5 | -10% | Brownish/Mauve | | | 6 | -8% | Brownish/Mauve | | | 7 | -5% | Brownish/Mauve | | | 8 | -2% | Grayish-Purple | Very close to zero. | | 9 | 0% | Grayish-Purple | Appears to be at the zero line. | | 10 | +2% | Grayish-Purple | First positive value. | | 11 | +8% | Light Blue | | | 12 | +12% | Light Blue | | | 13 | +15% | Light Blue | | | 14 | +18% | Light Blue | | | 15 | +20% | Light Blue | | | 16 | +25% | Medium Blue | | | 17 | +30% | Medium Blue | | | 18 | +38% | Medium Blue | | | 19 | +48% | Blue | | | 20 | +90% | Dark Blue | The highest value, showing a dramatic increase. | **Trend Verification:** The data series exhibits a consistent, monotonic upward trend from left to right. The slope is relatively gentle for the first 18 bars, followed by a sharp, non-linear increase for the final two bars, especially the last one. ### Key Observations 1. **Performance Spectrum:** The chart captures a wide performance spectrum, from significantly below (-35%) to substantially above (+90%) the human rater baseline. 2. **Clustering:** There are three apparent clusters based on color and value: * A **negative cluster** (bars 1-8, orange to mauve) performing below the human baseline. * A **near-zero cluster** (bars 8-10, grayish-purple) performing approximately at the human level. * A **positive cluster** (bars 11-20, blue shades) outperforming the human baseline. 3. **Outlier:** The rightmost bar (position 20, dark blue) is a significant positive outlier, with a value (~+90%) nearly double that of the second-highest bar (~+48%). 4. **Ordering:** The strict left-to-right ordering by value suggests the items on the x-axis have been sorted by this performance metric. ### Interpretation This chart likely visualizes the results of a benchmarking study ("CoC" possibly standing for "Chain of Code" or a similar evaluation framework) where various AI models or prompting strategies ("try Python except LM" may refer to a specific experimental condition) are tested on a task. Their performance is measured as a percentage difference from the average score of human raters. The data suggests a clear hierarchy of effectiveness. The majority of methods (the blue bars) outperform humans, with a few achieving dramatic superiority. The presence of methods that perform worse than humans (orange bars) indicates that not all approaches are successful for this task. The sharp spike at the end implies that one particular method or model is exceptionally well-suited to the challenge, representing a potential breakthrough or state-of-the-art result within this evaluation context. The color gradient, while not labeled, visually reinforces this performance continuum from poor (warm colors) to excellent (cool colors).

## Bar Chart: CoC (try Python except LM) ### Overview The chart visualizes the change in average human rater scores (Δ w.r.t. baseline) across different conditions related to Python programming, with a focus on excluding a component labeled "LM". The y-axis represents percentage changes, while the x-axis categorizes conditions. Bars transition from red (negative changes) to blue (positive changes), indicating a shift in human rater evaluations. ### Components/Axes - **Title**: "CoC (try Python except LM)" - **Y-Axis**: "Δ w.r.t. average human rater (%)" (range: -100 to 100) - **X-Axis**: Unlabeled categories (likely conditions or trials), with approximate 15–20 bars. - **Legend**: - Red: "Python" - Blue: "Python (except LM)" ### Detailed Analysis - **Negative Values (Red Bars)**: - First 5–7 bars show negative changes, ranging from **-30% to -50%**. - Values gradually increase toward zero (e.g., -20% to -10% in later negative bars). - **Positive Values (Blue Bars)**: - Transition begins around the 8th bar, with values rising from **5% to 20%**. - Steeper increase in the final 5 bars, peaking at **~95%** in the last bar. - **Color Consistency**: - Red bars align with "Python" (negative changes). - Blue bars align with "Python (except LM)" (positive changes). ### Key Observations 1. **Significant Shift**: The exclusion of "LM" correlates with a dramatic increase in positive human rater scores. 2. **Outlier**: The final bar’s value (~95%) is an outlier, suggesting a strong effect in the last condition. 3. **Gradual Improvement**: Early conditions show negative feedback, but later conditions (excluding LM) improve progressively. ### Interpretation The data suggests that removing "LM" from Python-related tasks leads to markedly higher human rater satisfaction. The sharp rise in the final bar implies that "LM" may have been a critical factor reducing performance or satisfaction in earlier conditions. This could indicate that "LM" introduces complexity, errors, or inefficiencies that negatively impact human evaluations. The trend highlights the importance of isolating components like "LM" to optimize user experience or task outcomes.