## Violin Plot: High School CS Accuracy ### Overview The image is a violin plot comparing the accuracy of different models on a "High School CS" task. The models vary in their use of Large Language Models (LLMs) and confidence measures. The plot shows the distribution of accuracy scores for each model. A horizontal dashed red line is present at approximately y=0.7. ### Components/Axes * **Title:** High School CS * **Y-axis:** Accuracy, ranging from 0.2 to 1.0 in increments of 0.2. * **X-axis:** Categorical, representing different models: * No LLM (Blue) * LLM (Orange) * LLM + Conf (Rand) (Green) * LLM + Conf (Query) (Red) * LLM + Conf (CT) (Purple) ### Detailed Analysis * **No LLM (Blue):** The distribution is wide, indicating a large variance in accuracy. The bulk of the data appears to be between 0.2 and 0.7, with a peak around 0.3-0.4. * **LLM (Orange):** The distribution is narrower than "No LLM," suggesting less variance. The bulk of the data is between 0.4 and 0.8, with a peak around 0.6-0.7. * **LLM + Conf (Rand) (Green):** The distribution is relatively narrow and centered higher than "LLM." The bulk of the data is between 0.5 and 0.9, with a peak around 0.7-0.8. * **LLM + Conf (Query) (Red):** The distribution is similar to "LLM + Conf (Rand)," but possibly with a slightly higher peak. The bulk of the data is between 0.6 and 1.0, with a peak around 0.7-0.8. * **LLM + Conf (CT) (Purple):** The distribution is similar to "LLM + Conf (Rand)" and "LLM + Conf (Query)." The bulk of the data is between 0.5 and 0.9, with a peak around 0.7. ### Key Observations * Using an LLM generally improves accuracy compared to "No LLM." * Adding a confidence measure ("Conf") to the LLM tends to further improve accuracy. * The different methods of incorporating confidence ("Rand," "Query," "CT") appear to yield similar accuracy distributions. * The horizontal red line is at approximately 0.7 accuracy. ### Interpretation The violin plot suggests that incorporating a Large Language Model (LLM) into the system improves accuracy on the "High School CS" task. Furthermore, using a confidence measure in conjunction with the LLM provides an additional boost in performance. The specific method of incorporating confidence (random, query-based, or CT) does not seem to significantly impact the overall accuracy distribution. The "No LLM" model has the lowest and most variable accuracy, indicating that the LLM is a crucial component for achieving higher and more consistent performance. The red line at 0.7 provides a visual reference point, showing which models consistently achieve accuracy above this threshold.

\n ## Violin Plot: High School CS Accuracy ### Overview The image presents a violin plot comparing the accuracy scores for different approaches to a High School Computer Science (CS) task. The approaches are: "No LLM", "LLM", "LLM + Conf (Rand)", "LLM + Conf (Query)", and "LLM + Conf (CT)". A horizontal dashed red line indicates a benchmark accuracy level. ### Components/Axes * **X-axis:** Categorical, representing the different approaches: "No LLM", "LLM", "LLM + Conf (Rand)", "LLM + Conf (Query)", "LLM + Conf (CT)". * **Y-axis:** Numerical, labeled "Accuracy", with a scale ranging from approximately 0.2 to 1.0, incrementing by 0.2. * **Violin Plots:** Each approach is represented by a violin plot, showing the distribution of accuracy scores. * **Horizontal Dashed Line:** A red dashed horizontal line is present at approximately y = 0.65, likely representing a threshold or baseline accuracy. ### Detailed Analysis The violin plots show the distribution of accuracy scores for each approach. * **No LLM (Blue):** The violin plot is centered around approximately 0.55, with a wide distribution ranging from approximately 0.2 to 1.0. The plot is relatively broad, indicating significant variability in accuracy. * **LLM (Orange):** The violin plot is centered around approximately 0.6, with a distribution ranging from approximately 0.3 to 0.9. It is narrower than the "No LLM" plot, suggesting less variability. * **LLM + Conf (Rand) (Green):** The violin plot is centered around approximately 0.7, with a distribution ranging from approximately 0.4 to 1.0. It appears slightly wider than the "LLM" plot. * **LLM + Conf (Query) (Red):** The violin plot is centered around approximately 0.65, with a distribution ranging from approximately 0.3 to 0.9. It is similar in shape to the "LLM" plot. * **LLM + Conf (CT) (Purple):** The violin plot is centered around approximately 0.7, with a distribution ranging from approximately 0.4 to 1.0. It is similar in shape to the "LLM + Conf (Rand)" plot. The red dashed line at approximately 0.65 serves as a visual benchmark. The "LLM + Conf (Rand)" and "LLM + Conf (CT)" approaches show a higher proportion of scores above this line compared to the other approaches. ### Key Observations * The "No LLM" approach has the widest distribution of accuracy scores, indicating the most variability. * The "LLM" approach shows an improvement in accuracy compared to "No LLM", with a narrower distribution. * Adding confidence information ("LLM + Conf") generally improves accuracy, particularly with the "Rand" and "CT" methods. * The "LLM + Conf (Rand)" and "LLM + Conf (CT)" approaches have the highest median accuracy and a significant portion of scores above the 0.65 benchmark. ### Interpretation The data suggests that incorporating Large Language Models (LLMs) improves accuracy on the High School CS task compared to not using an LLM. Furthermore, adding confidence information to the LLM-based approaches ("LLM + Conf") further enhances performance. The "Rand" and "CT" methods for incorporating confidence appear to be particularly effective. The violin plots reveal the distribution of accuracy scores, providing insights into the consistency of each approach. The wider distribution for "No LLM" indicates that performance is more variable without the assistance of an LLM. The narrower distributions for the LLM-based approaches suggest more consistent results. The horizontal dashed line likely represents a target accuracy or a baseline performance level. The fact that "LLM + Conf (Rand)" and "LLM + Conf (CT)" have a larger proportion of scores above this line indicates that these approaches are more likely to meet or exceed the desired performance threshold. The differences between the "Rand", "Query", and "CT" confidence methods are subtle but potentially meaningful. Further investigation would be needed to determine which method is most effective and why.

## Violin Plot: High School CS Accuracy Comparison ### Overview The image is a statistical visualization (violin plot) titled "High School CS" that compares the distribution of "Accuracy" scores across five different experimental conditions related to the use of Large Language Models (LLMs). The plot displays the probability density of the data at different values, with the width of each "violin" representing the frequency of data points at that accuracy level. ### Components/Axes * **Chart Title:** "High School CS" (centered at the top). * **Y-Axis:** * **Label:** "Accuracy" (rotated vertically on the left side). * **Scale:** Linear scale from 0.2 to 1.0, with major tick marks at 0.2, 0.4, 0.6, 0.8, and 1.0. * **X-Axis:** Represents five categorical conditions. The labels are positioned below each corresponding violin plot. 1. **No LLM** (far left, blue violin) 2. **LLM** (orange violin) 3. **LLM + Conf (Rand)** (green violin) 4. **LLM + Conf (Query)** (red violin) 5. **LLM + Conf (CT)** (far right, purple violin) * **Reference Line:** A horizontal red dashed line is drawn across the entire chart at an accuracy value of approximately **0.7**. This likely serves as a benchmark or baseline for comparison. ### Detailed Analysis Each violin plot shows the distribution of accuracy scores for its condition. The internal horizontal lines within each violin typically represent quartiles (e.g., median, 25th, 75th percentiles). 1. **No LLM (Blue):** * **Trend/Shape:** The distribution is heavily skewed towards lower accuracy. It is widest (most dense) between approximately 0.2 and 0.5, with a long, thin tail extending up to ~0.9. * **Key Values:** The median appears to be around **0.45**. The bulk of the data (interquartile range) lies between ~0.3 and ~0.6. 2. **LLM (Orange):** * **Trend/Shape:** The distribution is more symmetric and centered higher than "No LLM." It is widest around 0.6-0.7. * **Key Values:** The median is approximately **0.65**. The main density is concentrated between ~0.5 and ~0.8. 3. **LLM + Conf (Rand) (Green):** * **Trend/Shape:** The distribution is similar in shape to the "LLM" condition but shifted slightly upward. It is widest between 0.7 and 0.8. * **Key Values:** The median is near **0.72**, sitting just above the red reference line. The dense region spans from ~0.6 to ~0.85. 4. **LLM + Conf (Query) (Red):** * **Trend/Shape:** This distribution shows a clear upward shift. It is widest in the high-accuracy region between 0.8 and 0.9, indicating a high concentration of top scores. * **Key Values:** The median is the highest among all groups, at approximately **0.82**. The interquartile range is roughly 0.7 to 0.9. 5. **LLM + Conf (CT) (Purple):** * **Trend/Shape:** Very similar in profile to the "Query" condition, with a high-density peak between 0.8 and 0.9. It may be slightly narrower, suggesting marginally less variance. * **Key Values:** The median is also very high, around **0.81**. The distribution is concentrated between ~0.7 and ~0.9. ### Key Observations * **Clear Performance Hierarchy:** There is a visible, stepwise improvement in accuracy distributions from left to right: `No LLM` < `LLM` < `LLM + Conf (Rand)` < `LLM + Conf (Query)` ≈ `LLM + Conf (CT)`. * **Impact of Confidence Mechanisms:** All conditions using an LLM with a confidence mechanism ("Conf") outperform the plain "LLM" condition. The "Query" and "CT" methods show the most significant gains. * **Benchmark Comparison:** The red dashed line at ~0.7 accuracy is exceeded by the median of the top three conditions (`Rand`, `Query`, `CT`). The `No LLM` and plain `LLM` conditions have medians below this line. * **Variability:** The "No LLM" condition shows the greatest spread (from ~0.1 to ~0.9), indicating highly inconsistent performance. The top-performing conditions (`Query`, `CT`) have a tighter spread in the high-accuracy range, indicating more reliable high performance. ### Interpretation This chart presents compelling evidence for the efficacy of using LLMs, particularly when augmented with confidence estimation strategies, in the context of high school computer science tasks. * **The Core Finding:** Relying on no LLM (`No LLM`) leads to low and highly variable accuracy. Introducing a base LLM (`LLM`) provides a substantial and consistent boost. * **The Value of Confidence:** The key insight is that not all LLM integrations are equal. Adding a confidence mechanism—whether random (`Rand`), query-based (`Query`), or using a method abbreviated as `CT`—further improves both the median accuracy and the consistency of high performance. The `Query` and `CT` methods appear to be the most sophisticated and effective. * **Practical Implication:** For educational technology or assessment tools in high school CS, employing an LLM with an advanced confidence-filtering system (like `Query` or `CT`) is likely to yield the most accurate and reliable results, consistently surpassing the ~70% accuracy benchmark shown. The data suggests these systems can help students achieve higher accuracy outcomes more reliably.

## Violin Plot: High School CS Model Accuracy Comparison ### Overview The image presents a comparative analysis of model accuracy distributions across five configurations in a high school computer science context. Five violin plots are arranged horizontally, each representing a different model variant, with a red dashed reference line at 0.7 accuracy. ### Components/Axes - **X-axis**: Model configurations (categorical) - "No LLM" (blue) - "LLM" (orange) - "LLM + Conf (Rand)" (green) - "LLM + Conf (Query)" (red) - "LLM + Conf (CT)" (purple) - **Y-axis**: Accuracy (continuous scale from 0.0 to 1.0) - **Legend**: Right-aligned color-coded labels matching x-axis categories - **Reference Line**: Red dashed horizontal line at y=0.7 ### Detailed Analysis 1. **No LLM (Blue)**: - Distributed between 0.3-0.6 accuracy - Median ~0.5 (horizontal line within violin) - Wide distribution indicates high variability 2. **LLM (Orange)**: - Concentrated around 0.6-0.75 - Median ~0.65 - Narrower distribution than "No LLM" 3. **LLM + Conf (Rand) (Green)**: - Peaks at 0.7-0.8 - Median ~0.75 - Symmetrical distribution 4. **LLM + Conf (Query) (Red)**: - Distributed 0.7-0.85 - Median ~0.78 - Slightly skewed toward higher values 5. **LLM + Conf (CT) (Purple)**: - Peaks at 0.8-0.9 - Median ~0.85 - Most concentrated distribution ### Key Observations - All "LLM + Conf" variants exceed the 0.7 reference line - "LLM + Conf (CT)" achieves highest median accuracy (~0.85) - "No LLM" shows lowest and most variable performance - Confidence augmentation improves accuracy by 0.15-0.25 compared to base LLM - "LLM + Conf (Rand)" and "(Query)" show similar performance (~0.75 median) ### Interpretation The data demonstrates that confidence augmentation consistently improves model accuracy in high school CS applications. The "CT" (likely "Contextual Tuning") method yields the most significant gains, suggesting specialized confidence mechanisms enhance performance. The red reference line at 0.7 appears to represent a performance threshold, with all confidence-augmented models exceeding this benchmark. The progressive improvement from "LLM" to "LLM + Conf (CT)" indicates that confidence augmentation strategies are additive and context-dependent, with CT providing the most substantial benefits. The variability in "No LLM" suggests baseline models struggle with consistency in this domain.