## Heatmap: MIND - Long-to-Short - Qwen-2.5 7B ### Overview The image is a heatmap displaying the accuracy (%) of a model named "MIND - Long-to-Short - Qwen-2.5 7B" across different types and lengths. The x-axis represents the length, ranging from 0 to 11. The y-axis represents the type, ranging from 1 to 7. The color intensity represents the accuracy percentage, with lighter shades of green indicating lower accuracy and darker shades indicating higher accuracy. Numerical values are displayed within each cell of the heatmap. ### Components/Axes * **Title:** MIND - Long-to-Short - Qwen-2.5 7B * **X-axis:** * Label: Length * Scale: 0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11 * **Y-axis:** * Label: Type * Scale: 1, 2, 3, 4, 5, 6, 7 * **Colorbar (right side):** * Label: Accuracy (%) * Scale: 0, 20, 40, 60, 80, 100 (corresponding to the green color gradient) ### Detailed Analysis or Content Details The heatmap presents accuracy values for different "Type" and "Length" combinations. * **Type 1:** * Length 0: 2.0% * Length 1: 27.0% * Length 2: 48.3% * Length 3: 63.0% * Length 4: 68.3% * **Type 2:** * Length 1: 56.0% * Length 2: 92.3% * Length 3: 98.7% * Length 4: 99.0% * Length 5: 97.7% * **Type 3:** * Length 0: 28.3% * Length 1: 99.0% * Length 2: 98.7% * Length 3: 99.0% * Length 4: 97.0% * **Type 4:** * Length 0: 35.0% * Length 1: 70.0% * Length 2: 86.7% * Length 3: 90.7% * Length 4: 89.0% * **Type 5:** * Length 7: 71.3% * Length 8: 79.0% * Length 9: 84.0% * Length 10: 99.3% * Length 11: 97.3% * **Type 6:** * Length 0: 14.0% * Length 1: 98.3% * Length 2: 98.3% * Length 3: 99.7% * Length 4: 99.7% * **Type 7:** * Length 0: 1.0% * Length 1: 84.7% * Length 2: 98.0% * Length 3: 99.0% * Length 4: 98.7% ### Key Observations * Accuracy varies significantly across different types and lengths. * Types 2, 3, 6, and 7 generally exhibit high accuracy for lengths 1-4. * Types 1 and 4 show lower accuracy for length 0. * Type 5 shows accuracy for lengths 7-11. ### Interpretation The heatmap visualizes the performance of the "MIND - Long-to-Short - Qwen-2.5 7B" model. The model's accuracy is highly dependent on both the "Type" and "Length" parameters. Some types consistently achieve high accuracy across different lengths, while others are more sensitive to length variations. The data suggests that the model performs best with Types 2, 3, 6, and 7 for lengths 1-4. Type 5 performs well for lengths 7-11. Types 1 and 4 have lower accuracy for length 0, indicating a potential weakness in handling very short sequences for these types. The heatmap provides a clear overview of the model's strengths and weaknesses, allowing for targeted improvements and optimization.

## Heatmap: MIND - Long-to-Short - Qwen-2.5 7B ### Overview This image presents a heatmap visualizing the accuracy of the Qwen-2.5 7B model on the MIND (presumably a dataset or task) for long-to-short generation, categorized by 'Type' and 'Length'. The heatmap uses a color gradient to represent accuracy, ranging from approximately 0% (lightest color) to 100% (darkest color). ### Components/Axes * **Title:** MIND - Long-to-Short - Qwen-2.5 7B (centered at the top) * **X-axis:** Length, ranging from 0 to 11, with markers at each integer value. * **Y-axis:** Type, ranging from 1 to 7, with markers at each integer value. * **Color Scale/Legend:** Located on the right side of the heatmap, representing Accuracy (%) from 0 to 100. The color gradient transitions from light green to dark green. * **Data Points:** Each cell in the grid represents the accuracy for a specific combination of Type and Length. The accuracy value is displayed within each cell. ### Detailed Analysis The heatmap displays accuracy values for 7 Types and 12 Lengths. The values are as follows (organized by Type, then Length): * **Type 1:** * Length 0: 2.0% * Length 1: 27.0% * Length 2: 48.3% * Length 3: 63.0% * Length 4: 68.3% * **Type 2:** * Length 0: 56.0% * Length 1: 92.3% * Length 2: 98.7% * Length 3: 99.0% * Length 4: 99.7% * **Type 3:** * Length 0: 28.3% * Length 1: 99.0% * Length 2: 98.7% * Length 3: 99.0% * Length 4: 97.0% * **Type 4:** * Length 0: 35.0% * Length 1: 70.0% * Length 2: 86.7% * Length 3: 90.7% * Length 4: 89.0% * **Type 5:** * Length 7: 71.3% * Length 8: 79.0% * Length 9: 84.0% * Length 10: 99.3% * Length 11: 97.3% * **Type 6:** * Length 0: 14.0% * Length 1: 98.3% * Length 2: 98.3% * Length 3: 99.7% * Length 4: 99.7% * **Type 7:** * Length 0: 1.0% * Length 1: 84.7% * Length 2: 98.0% * Length 3: 99.0% * Length 4: 98.7% **Trends:** * For most Types, accuracy generally *increases* with increasing Length, up to a point. * Type 5 is only defined for Lengths 7-11. * Type 1 and Type 7 have very low accuracy at Length 0. * Types 2, 3, 6, and 7 achieve very high accuracy (above 98%) for Lengths 1-4. ### Key Observations * The model performs poorly on Type 1 and Type 7 when the length is 0. * Accuracy is generally high for Types 2, 3, 6, and 7 across most lengths. * Type 5 shows a gradual increase in accuracy from Length 7 to Length 10, then a slight decrease at Length 11. * The heatmap is not fully populated; there are missing data points for some Type/Length combinations. ### Interpretation This heatmap demonstrates the performance of the Qwen-2.5 7B model on the MIND dataset for long-to-short generation tasks, broken down by 'Type' and 'Length'. The high accuracy scores for many Type/Length combinations suggest the model is effective at this task, particularly for Types 2, 3, 6, and 7. The low accuracy for Type 1 and Type 7 at length 0 could indicate difficulties with very short sequences or specific characteristics of those types. The missing data points suggest that the model was not evaluated for all possible combinations of Type and Length. The variation in performance across different lengths and types suggests that the model's effectiveness is sensitive to these factors. Further investigation would be needed to understand the nature of the 'Types' and why certain types exhibit lower accuracy at specific lengths. The data suggests that the model is more robust to longer sequences, but this is not universally true, as seen with Type 5.

## Heatmap: MIND - Long-to-Short - Qwen-2.5 7B ### Overview This image is a heatmap visualizing the accuracy (in percentage) of a model named "Qwen-2.5 7B" on a task or dataset referred to as "MIND - Long-to-Short". The heatmap plots accuracy against two categorical variables: "Type" (vertical axis) and "Length" (horizontal axis). The color intensity represents accuracy, with a scale from 0% (lightest) to 100% (darkest green). ### Components/Axes * **Title:** "MIND - Long-to-Short - Qwen-2.5 7B" * **Vertical Axis (Y-axis):** Labeled "Type". It contains 7 discrete categories, numbered 1 through 7. * **Horizontal Axis (X-axis):** Labeled "Length". It contains 12 discrete categories, numbered 0 through 11. * **Legend/Color Bar:** Located on the right side of the chart. It is a vertical gradient bar labeled "Accuracy (%)". The scale runs from 0 at the bottom to 100 at the top, with intermediate markers at 20, 40, 60, and 80. The color transitions from a very light, almost white-green at 0% to a deep, dark green at 100%. ### Detailed Analysis The following table reconstructs the data presented in the heatmap. Each cell contains the accuracy value for a specific Type and Length combination. Cells with no value indicate missing data for that combination. | Type \ Length | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | **1** | 2.0 | 27.0 | 48.3 | 63.0 | 68.3 | | | | | | | | | **2** | | 56.0 | 92.3 | 98.7 | 99.0 | 97.7 | | | | | | | | **3** | 28.3 | 99.0 | 98.7 | 99.0 | 97.0 | | | | | | | | | **4** | | 35.0 | 70.0 | 86.7 | 90.7 | 89.0 | | | | | | | | **5** | | | | | | | | 71.3 | 79.0 | 84.0 | 99.3 | 97.3 | | **6** | 14.0 | 98.3 | 98.3 | 99.7 | 99.7 | | | | | | | | | **7** | 1.0 | 84.7 | 98.0 | 99.0 | 98.7 | | | | | | | | **Spatial & Color Grounding:** * The highest accuracy values (≥99.0%) are represented by the darkest green cells. These are found in: * Type 2 at Lengths 3 (98.7%) and 4 (99.0%). * Type 3 at Lengths 1 (99.0%) and 3 (99.0%). * Type 6 at Lengths 3 (99.7%) and 4 (99.7%). * Type 7 at Length 3 (99.0%). * Type 5 at Length 10 (99.3%). * The lowest accuracy values (≤2.0%) are represented by the lightest cells. These are found in: * Type 1 at Length 0 (2.0%). * Type 7 at Length 0 (1.0%). ### Key Observations 1. **General Trend:** For most Types (1, 2, 3, 4, 6, 7), accuracy increases significantly as "Length" increases from 0 or 1, often plateauing at a very high level (>90%) by Length 3 or 4. 2. **Type-Specific Performance:** * **Type 1** shows the most gradual improvement, starting very low (2.0%) and only reaching 68.3% by Length 4. * **Types 2, 3, 6, and 7** achieve near-perfect accuracy (>98%) very quickly, often by Length 2 or 3. * **Type 4** shows strong improvement but peaks at 90.7% (Length 4), slightly lower than the top performers. * **Type 5** is an outlier in data distribution. It has no recorded data for Lengths 0-6. Its performance starts at 71.3% (Length 7) and improves to a peak of 99.3% at Length 10. 3. **Data Sparsity:** The heatmap is not a complete grid. Data for Types 1-4 and 6-7 is concentrated on the left side (Lengths 0-5), while data for Type 5 is concentrated on the right side (Lengths 7-11). There is no overlap in the "Length" values between these two groups. ### Interpretation This heatmap likely evaluates the performance of the Qwen-2.5 7B model on a summarization or compression task ("Long-to-Short") within the MIND benchmark. The "Type" axis probably represents different categories of input documents or summarization tasks, while "Length" likely corresponds to the target summary length or a compression ratio. * **What the data suggests:** The model's ability to generate accurate short summaries is highly dependent on both the document type and the desired output length. For most document types, providing a slightly longer target summary (Length 3 or 4) yields a dramatic improvement in accuracy compared to very short targets (Length 0 or 1). * **Relationship between elements:** The stark separation between the data for Types 1-4/6-7 and Type 5 suggests these groups may represent fundamentally different task categories. Type 5 tasks might inherently require longer summaries (hence data only from Length 7 onward) or belong to a different subset of the benchmark. * **Notable patterns:** The near-perfect accuracy for several types at moderate lengths indicates the model is highly capable for those specific tasks. The poor performance of Type 1 at all lengths suggests it is a particularly challenging category for this model. The outlier status of Type 5 warrants further investigation into its definition within the MIND benchmark.

# Technical Document Extraction: MIND - Long-to-Short - Qwen-2.5 7B ## 1. Chart Identification - **Chart Type**: Heatmap - **Title**: "MIND - Long-to-Short - Qwen-2.5 7B" - **Language**: English (no non-English text detected) ## 2. Axis Labels & Markers - **X-Axis (Horizontal)**: - Label: "Length" - Values: 0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11 - **Y-Axis (Vertical)**: - Label: "Type" - Values: 1, 2, 3, 4, 5, 6, 7 - **Colorbar**: - Label: "Accuracy (%)" - Range: 0% (lightest green) to 100% (darkest green) ## 3. Data Categories - **Rows (Type)**: 1–7 - **Columns (Length)**: 0–11 - **Data Points**: Accuracy percentages (%) for each (Type, Length) pair ## 4. Data Table Reconstruction | Type \ Length | 0 | 1 | 2 | 3 | 4 | 5 | 7 | 8 | 9 | 10 | 11 | |---------------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------| | 1 | 2.0 | 27.0 | 48.3 | 63.0 | 68.3 | - | - | - | - | - | - | | 2 | - | 56.0 | 92.3 | 98.7 | 99.0 | 97.7 | - | - | - | - | - | | 3 | 28.3 | 99.0 | 98.7 | 99.0 | 97.0 | - | - | - | - | - | - | | 4 | 35.0 | 70.0 | 86.7 | 90.7 | 89.0 | - | - | - | - | - | - | | 5 | - | - | - | - | - | - | 71.3 | 79.0 | 84.0 | 99.3 | 97.3 | | 6 | 14.0 | 98.3 | 98.3 | 99.7 | 99.7 | - | - | - | - | - | - | | 7 | 1.0 | 84.7 | 98.0 | 99.0 | 98.7 | - | - | - | - | - | - | ## 5. Key Trends & Observations - **General Pattern**: - Accuracy increases with Length for most Types until a peak, then declines. - Types 2, 3, 6, and 7 show high accuracy (97–99%) at mid-to-high Lengths. - Type 5 has lower accuracy (71–84%) at mid Lengths but peaks at Length 10 (99.3%). - **Notable Peaks**: - Type 2: Length 4 (99.0%) - Type 3: Length 1 (99.0%) - Type 6: Length 4 (99.7%) - Type 7: Length 3 (99.0%) - **Lowest Accuracy**: - Type 7, Length 0 (1.0%) - Type 1, Length 0 (2.0%) ## 6. Legend & Spatial Grounding - **Legend**: Colorbar on the right side of the heatmap. - **Color Correlation**: Darker green = higher accuracy (e.g., 99.7% = darkest green). ## 7. Component Isolation - **Header**: Chart title at the top. - **Main Chart**: 7x11 grid of accuracy percentages. - **Footer**: Colorbar with "Accuracy (%)" label. ## 8. Data Validation - All values match the colorbar's gradient (e.g., 99.7% = dark green). - Missing values (marked as "-") indicate no data for specific (Type, Length) pairs. ## 9. Summary The heatmap visualizes the accuracy of the Qwen-2.5 7B model's long-to-short attention mechanism across different input lengths (0–11) and attention types (1–7). Accuracy generally improves with longer input lengths until a peak, after which it declines. Types 2, 3, 6, and 7 demonstrate the highest performance, while Type 5 underperforms at mid-lengths.