## Heatmap: Attention Head Categories Across Layers
### Overview
The image presents a series of heatmaps visualizing the distribution of attention head categories across different layers of a model. Each heatmap represents a specific category or an aggregate of categories. The x-axis represents the layer number (0-80), and the y-axis represents the attention head (0-60). The color of each data point indicates the category the attention head belongs to, as defined by the legend.
### Components/Axes
* **Titles:** The heatmaps are titled as follows: "All Categories", "Algorithmic", "Knowledge", "Linguistic", and "Translation".
* **X-axis:** Labeled "layer", with ticks at 0, 16, 32, 48, 64, and 80.
* **Y-axis:** Labeled "head", with ticks at 0, 12, 24, 36, 48, and 60.
* **Legend (located to the right of the "All Categories" heatmap):**
* Pink: "4 categories"
* Brown: "3 categories"
* Purple: "2 categories"
* Red: "Translation"
* Green: "Linguistic"
* Orange: "Knowledge"
* Blue: "Algorithmic"
* Light Gray: "Unclassified"
### Detailed Analysis
**1. All Categories:**
* This heatmap shows the distribution of all categories.
* The distribution is sparse, with most heads belonging to one category.
* There is a concentration of "Knowledge" (orange) and "Algorithmic" (blue) heads in the earlier layers (approximately layers 16-48).
* "Linguistic" (green) heads are more prevalent in the later layers (approximately layers 48-80).
* "Translation" (red) heads are sparsely distributed.
* The "4 categories" (pink), "3 categories" (brown), and "2 categories" (purple) are very sparse.
**2. Algorithmic:**
* This heatmap isolates the "Algorithmic" category (blue).
* The "Algorithmic" heads are primarily concentrated in the earlier layers (approximately layers 16-48).
* There are a few "Algorithmic" heads in the later layers, but they are less frequent.
**3. Knowledge:**
* This heatmap isolates the "Knowledge" category (orange).
* The "Knowledge" heads are also concentrated in the earlier layers (approximately layers 16-48).
* The distribution is more spread out compared to "Algorithmic".
**4. Linguistic:**
* This heatmap isolates the "Linguistic" category (green).
* The "Linguistic" heads are more prevalent in the later layers (approximately layers 48-80).
* There are fewer "Linguistic" heads in the earlier layers.
**5. Translation:**
* This heatmap isolates the "Translation" category (red).
* The "Translation" heads are sparsely distributed across all layers.
* There appears to be a slight concentration in the later layers (approximately layers 64-80).
### Key Observations
* "Algorithmic" and "Knowledge" categories are more active in the earlier layers.
* "Linguistic" category is more active in the later layers.
* "Translation" category is sparsely distributed.
* The "All Categories" heatmap shows a mix of all categories, with a clear separation of "Algorithmic/Knowledge" and "Linguistic" across layers.
* The "Unclassified" category is not explicitly visualized in the individual category heatmaps, but its presence can be inferred from the "All Categories" heatmap.
### Interpretation
The heatmaps suggest that different layers of the model specialize in different types of tasks. The earlier layers (16-48) seem to focus on "Algorithmic" and "Knowledge" related tasks, while the later layers (48-80) focus on "Linguistic" tasks. The "Translation" category appears to be more distributed, suggesting that it might be integrated across different layers.
The distribution of attention heads across layers could reflect the hierarchical nature of the model, where earlier layers learn lower-level features and later layers learn higher-level features. The concentration of "Algorithmic" and "Knowledge" heads in earlier layers might indicate that these tasks require more fundamental processing, while "Linguistic" tasks require more complex processing in later layers.
The sparsity of the "Translation" category could indicate that translation-related information is integrated across different layers, or that it is less prominent compared to other categories. The "Unclassified" category might represent attention heads that do not fall into any of the defined categories, or that are involved in more general tasks.
