## Violin Plot: High School Biology Accuracy ### Overview The image is a violin plot comparing the accuracy of different models on a high school biology task. The models include a baseline with no LLM, an LLM alone, and LLMs with confidence measures (random, query-based, and CT-based). The y-axis represents accuracy, and the x-axis represents the different models. A red dashed line indicates a reference accuracy level. ### Components/Axes * **Title:** High School Biology * **Y-axis:** Accuracy, ranging from 0.0 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) * **Horizontal Dashed Red Line:** Appears to be a reference line at approximately 0.67 accuracy. * **Violin Plot Components:** Each violin plot shows the distribution of accuracy for each model. The black dashed lines within each violin plot represent quartiles. ### Detailed Analysis * **No LLM (Blue):** The "No LLM" model has a wide distribution, with the majority of the data concentrated between 0.3 and 0.6 accuracy. * **LLM (Orange):** The "LLM" model has a distribution centered around 0.6 accuracy, with a narrower spread than the "No LLM" model. * **LLM + Conf (Rand) (Green):** The "LLM + Conf (Rand)" model has a distribution similar to the "LLM" model, but slightly shifted upwards, with the majority of the data concentrated between 0.5 and 0.8 accuracy. * **LLM + Conf (Query) (Red):** The "LLM + Conf (Query)" model has a distribution centered around 0.7 accuracy, with a noticeable tail extending down to lower accuracy values. * **LLM + Conf (CT) (Purple):** The "LLM + Conf (CT)" model has a distribution centered around 0.65 accuracy, with a narrower spread than the other LLM-based models. ### Key Observations * The addition of an LLM generally improves accuracy compared to the "No LLM" baseline. * Confidence measures, particularly the query-based approach, appear to further enhance accuracy. * The "LLM + Conf (Query)" model has the highest median accuracy but also exhibits a wider range of performance. * The red dashed line at approximately 0.67 accuracy seems to be a target or threshold. ### Interpretation The violin plot suggests that using an LLM improves the accuracy of the high school biology task. Furthermore, incorporating confidence measures, especially using a query-based approach, can lead to even better performance. However, the wider distribution of the "LLM + Conf (Query)" model indicates that its performance may be more variable. The "No LLM" model has the lowest accuracy, indicating the value of using an LLM for this task. The red line may represent a minimum acceptable accuracy, and the models that consistently perform above this line are likely preferred.

\n ## Violin Plot: High School Biology Accuracy ### Overview This image presents a violin plot comparing the accuracy of different approaches to answering high school biology questions. The approaches are: "No LLM", "LLM", "LLM + Conf (Rand)", "LLM + Conf (Query)", and "LLM + Conf (CT)". The y-axis represents accuracy, ranging from approximately 0.2 to 1.0. A horizontal dashed red line is present across the plot, likely representing a baseline or threshold accuracy. ### Components/Axes * **Title:** "High School Biology" * **X-axis:** Categorical, representing the different approaches: * "No LLM" * "LLM" * "LLM + Conf (Rand)" * "LLM + Conf (Query)" * "LLM + Conf (CT)" * **Y-axis:** "Accuracy", with a scale ranging from approximately 0.2 to 1.0. * **Horizontal Line:** A dashed red line at approximately y = 0.6. * **Violin Plots:** Five violin plots, one for each category on the x-axis. Each violin plot shows the distribution of accuracy scores for that approach. ### Detailed Analysis The violin plots show the distribution of accuracy scores for each approach. * **No LLM (Blue):** The violin plot is centered around approximately 0.65, with a range from roughly 0.3 to 1.0. The plot is relatively wide, indicating a significant spread in accuracy scores. * **LLM (Orange):** The violin plot is centered around approximately 0.6, with a range from roughly 0.3 to 0.9. It is slightly narrower than the "No LLM" plot. * **LLM + Conf (Rand) (Green):** The violin plot is centered around approximately 0.7, with a range from roughly 0.4 to 0.95. It appears to have a slightly higher median than the previous two. * **LLM + Conf (Query) (Red):** The violin plot is centered around approximately 0.55, with a range from roughly 0.3 to 0.8. It is narrower than the "No LLM" and "LLM" plots. * **LLM + Conf (CT) (Purple):** The violin plot is centered around approximately 0.62, with a range from roughly 0.35 to 0.9. It is similar in shape to the "LLM" plot. The dashed red line at approximately 0.6 appears to be a benchmark. The "No LLM", "LLM + Conf (Rand)", and "LLM + Conf (CT)" distributions largely exceed this line, while "LLM" and "LLM + Conf (Query)" have significant portions of their distributions below it. ### Key Observations * The "LLM + Conf (Rand)" approach appears to have the highest median accuracy. * The "LLM + Conf (Query)" approach appears to have the lowest median accuracy. * The "No LLM" approach performs comparably to the "LLM + Conf (Rand)" and "LLM + Conf (CT)" approaches. * The "LLM" approach performs slightly worse than the "No LLM" approach. * There is considerable variability in accuracy scores within each approach, as indicated by the width of the violin plots. ### Interpretation The data suggests that simply adding a Large Language Model (LLM) does not necessarily improve accuracy compared to not using one ("No LLM"). However, incorporating confidence measures with the LLM can impact performance, depending on the method used for calculating confidence. The "LLM + Conf (Rand)" approach, which likely uses a random confidence score, shows the highest accuracy. The "LLM + Conf (Query)" approach, which likely uses a query-based confidence score, performs the worst. This could indicate that the method for determining confidence is crucial for the effectiveness of the LLM. The horizontal line at 0.6 likely represents a passing threshold or a baseline performance level. The fact that some approaches consistently exceed this line suggests they are providing value. The variability within each approach suggests that the performance is not consistent and may depend on the specific questions being asked. Further investigation is needed to understand why the "LLM + Conf (Query)" approach performs so poorly and to optimize the confidence calculation method.

## Violin Plot: High School Biology Accuracy by LLM Configuration ### Overview The image is a violin plot comparing the distribution of accuracy scores for five different configurations of a Large Language Model (LLM) system on a "High School Biology" task. A violin plot combines a box plot (showing median and interquartile range) with a kernel density plot (showing the probability density of the data at different values). The plot visualizes how the central tendency and spread of accuracy vary across the configurations. ### Components/Axes * **Chart Title:** "High School Biology" (centered at the top). * **Y-Axis:** * **Label:** "Accuracy" (rotated vertically on the left side). * **Scale:** Linear scale from 0.0 to 1.0. * **Tick Marks:** Major ticks at 0.0, 0.2, 0.4, 0.6, 0.8, and 1.0. * **X-Axis:** * **Categories (from left to right):** 1. "No LLM" 2. "LLM" 3. "LLM + Conf (Rand)" 4. "LLM + Conf (Query)" 5. "LLM + Conf (CT)" * **Reference Line:** A horizontal red dashed line is drawn across the plot at the y-axis value of **0.7**. * **Legend:** There is no separate legend box. The five categories on the x-axis serve as the legend, each associated with a distinct colored violin plot. * "No LLM": Blue * "LLM": Orange * "LLM + Conf (Rand)": Green * "LLM + Conf (Query)": Red * "LLM + Conf (CT)": Purple ### Detailed Analysis Each violin plot shows the distribution of accuracy scores for its category. The width of the violin at a given y-value represents the density (frequency) of data points at that accuracy level. Inside each violin, a thin black line represents the interquartile range (IQR), and a white dot or small horizontal line represents the median. 1. **No LLM (Blue):** * **Trend/Shape:** The distribution is heavily skewed towards lower accuracy. It is widest (has the highest density) between approximately 0.2 and 0.5, with a long, thin tail extending up to ~0.9. * **Key Values (Approximate):** Median appears to be around **0.45**. The bulk of the data (IQR) lies between ~0.3 and ~0.65. The range spans from near 0.0 to ~0.9. 2. **LLM (Orange):** * **Trend/Shape:** The distribution is more symmetric and centered higher than "No LLM". It is widest around 0.6-0.8, indicating most scores cluster in this range. * **Key Values (Approximate):** Median is approximately **0.7**. The IQR is roughly between 0.6 and 0.8. The range is from ~0.2 to ~0.95. 3. **LLM + Conf (Rand) (Green):** * **Trend/Shape:** This distribution is tall and relatively narrow, indicating high variance but with a central peak. It is widest around 0.6-0.8, similar to "LLM", but with a more pronounced peak and longer tails. * **Key Values (Approximate):** Median is near **0.7**. The IQR spans from ~0.55 to ~0.8. The range is very wide, from ~0.1 to nearly 1.0. 4. **LLM + Conf (Query) (Red):** * **Trend/Shape:** The distribution is wide in the middle (0.6-0.8) but has a very long, thin tail extending down to low accuracy scores, suggesting a subset of poor performances. * **Key Values (Approximate):** Median is around **0.7**. The IQR is between ~0.6 and ~0.8. The range is extensive, from ~0.1 to ~0.95. 5. **LLM + Conf (CT) (Purple):** * **Trend/Shape:** This is the most compact and highest-performing distribution. It is widest between 0.7 and 0.85, with a shorter tail extending downward compared to the other "Conf" methods. * **Key Values (Approximate):** Median is the highest, approximately **0.78**. The IQR is tight, between ~0.7 and ~0.85. The range is from ~0.4 to ~0.9. ### Key Observations * **Performance Hierarchy:** The "LLM + Conf (CT)" configuration shows the highest median accuracy and the most consistent performance (narrowest IQR). "No LLM" has the lowest median and a distribution skewed toward failure. * **Effect of LLM:** Simply adding an LLM ("LLM" category) dramatically shifts the entire distribution upward compared to "No LLM". * **Effect of Confidence Calibration ("Conf"):** All three "Conf" methods maintain a median accuracy around or above the 0.7 reference line. However, they introduce greater variance (wider ranges) compared to the base "LLM" configuration, particularly in the lower tails. * **Variance Comparison:** "LLM + Conf (Rand)" and "LLM + Conf (Query)" exhibit the largest spreads, with minimum scores near 0.1. "LLM + Conf (CT)" has a higher floor (~0.4). * **Reference Line:** The red dashed line at 0.7 serves as a visual benchmark. The medians of all LLM-based configurations are at or above this line, while the "No LLM" median is well below it. ### Interpretation This chart demonstrates the impact of different AI assistance strategies on high school biology task accuracy. * **Baseline vs. AI Assistance:** The stark contrast between "No LLM" and all other categories provides strong evidence that using an LLM significantly improves performance on this task. The "No LLM" distribution suggests that without AI, performance is highly variable and often poor. * **Calibration Trade-offs:** Adding confidence calibration ("Conf") to the LLM does not consistently improve the *median* score over the base "LLM" but appears to alter the *distribution* of outcomes. The "Rand" and "Query" methods seem to introduce instability, leading to both high and very low scores. This could indicate that these calibration methods are sometimes helpful but other times detrimental, perhaps due to overconfidence or misguidance. * **Superior Method:** The "LLM + Conf (CT)" method appears most effective. It not only achieves the highest median accuracy but also reduces the risk of very low scores (higher minimum), suggesting it is a more robust and reliable calibration technique for this domain. * **The 0.7 Benchmark:** The red line likely represents a target proficiency threshold (e.g., a passing grade or human expert baseline). The data shows that while an unaided LLM often meets this threshold, adding the right calibration ("CT") makes meeting it more consistent. In summary, the data suggests that for high school biology tasks, employing an LLM is highly beneficial, and using a specific confidence calibration method ("CT") can further optimize both the average performance and the reliability of the system.

## Violin Plot: High School Biology Model Accuracy Comparison ### Overview The image presents a comparative analysis of model accuracy distributions across five configurations in a high school biology context. Five colored violin plots represent different model setups, with a red dashed threshold line at 0.65 accuracy. The visualization emphasizes distributional characteristics (medians, quartiles, and spread) rather than just mean values. ### 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 metric (continuous, 0.0-1.0 scale) - **Legend**: Color-coded model configurations (implicitly mapped) - **Threshold line**: Red dashed horizontal line at 0.65 accuracy ### Detailed Analysis 1. **No LLM (blue)**: - Wide distribution with heavy lower tail - Median accuracy ~0.4 (black line) - Interquartile range (IQR) spans 0.3-0.5 - Long tail extends to 0.8 but with low density 2. **LLM (orange)**: - Symmetrical distribution centered ~0.6 - Median ~0.6 with IQR 0.5-0.7 - Moderate spread with slight right skew 3. **LLM + Conf (Rand) (green)**: - Similar median to LLM (~0.6) - Narrower IQR (0.55-0.65) - More concentrated distribution than base LLM 4. **LLM + Conf (Query) (red)**: - Highest median (~0.65) - Tightest distribution (IQR 0.6-0.7) - Significant portion above threshold line 5. **LLM + Conf (CT) (purple)**: - Lowest median (~0.55) - Bimodal distribution with peaks at 0.5 and 0.6 - Wide spread with heavy lower tail ### Key Observations - **Threshold performance**: Only LLM + Conf (Query) shows substantial representation above the 0.65 threshold - **Distribution characteristics**: - No LLM exhibits highest variability (widest spread) - LLM + Conf (CT) shows unusual bimodality - Confidence mechanisms generally reduce spread but with mixed median effects - **Color-legend alignment**: All colors match their respective categories without ambiguity ### Interpretation The data suggests that: 1. **Confidence mechanisms improve consistency**: LLM + Conf variants show narrower distributions than base LLM, indicating more stable performance 2. **Query method optimization**: The Query configuration achieves highest median accuracy and best threshold penetration 3. **CT method limitations**: The CT configuration underperforms other variants despite confidence mechanisms 4. **Baseline performance**: No LLM shows poorest performance with highest variability The visualization reveals that while confidence mechanisms generally improve model consistency, their effectiveness depends on implementation method. The Query approach demonstrates optimal balance between accuracy and reliability, while the CT method introduces unexpected performance degradation despite confidence integration. The red threshold line serves as a critical benchmark, showing that only one configuration consistently meets the 0.65 accuracy standard.