## Heatmaps: Layer vs. Head Activation by Category
### Overview
The image presents five heatmaps, each visualizing the activation levels of different categories (Algorithmic, Knowledge, Linguistic, Translation, and All Categories) across layers (0-32) and heads (0-32). The activation level is represented by color intensity, with a legend indicating the number of categories present at each point. The heatmaps aim to show where different categories are most active within the model's layers and heads.
### Components/Axes
* **X-axis (Layer):** Represents the layer number, ranging from 0 to 32, with increments of 2.
* **Y-axis (Head):** Represents the head number, ranging from 0 to 32, with increments of 6.
* **Color:** Represents the number of categories activated at a given layer and head. The legend indicates:
* 1 category: Lightest color (appears white/very pale)
* 2 categories: Light blue/green
* 3 categories: Medium blue/green
* 4 categories: Darkest color (dark green)
* **Heatmaps:** Five separate heatmaps, each focusing on a specific category or all categories.
* "All Categories"
* "Algorithmic"
* "Knowledge"
* "Linguistic"
* "Translation"
### Detailed Analysis or Content Details
**1. All Categories:**
* The heatmap shows a dense distribution of activations across layers and heads.
* The highest activation (4 categories) is concentrated around layers 18-24 and heads 6-18.
* There's a noticeable cluster of 3-category activations around layers 0-6 and heads 0-12.
* Lower activations (1-2 categories) are scattered throughout the remaining areas.
**2. Algorithmic:**
* Activations are primarily concentrated in layers 0-24.
* The highest activation (4 categories) is not present.
* The most frequent activation level is 2 categories, observed around layers 6-18 and heads 0-12.
* A sparse distribution of 1 and 3 category activations is visible.
**3. Knowledge:**
* Activations are concentrated in layers 18-24.
* The highest activation (4 categories) is present in a small region around layer 24 and head 0-6.
* The majority of activations are at the 2 and 3 category levels, distributed across layers 18-24 and heads 0-18.
**4. Linguistic:**
* Activations are heavily concentrated in layers 18-30.
* The highest activation (4 categories) is present around layers 18-24 and heads 0-12.
* A significant number of 3-category activations are observed across layers 18-30 and heads 0-18.
* Lower activations (1-2 categories) are scattered throughout.
**5. Translation:**
* Activations are primarily concentrated in layers 18-30.
* The highest activation (4 categories) is present around layers 18-24 and heads 0-6.
* A strong cluster of 3-category activations is observed around layers 24-30 and heads 0-12.
* The heatmap shows a relatively sparse distribution of 1 and 2 category activations.
### Key Observations
* The "All Categories" heatmap shows the most widespread activation, indicating a general level of activity across all layers and heads.
* "Algorithmic" activations are relatively sparse and concentrated in the earlier layers.
* "Knowledge," "Linguistic," and "Translation" categories exhibit stronger activations in the later layers (18-30).
* The "Translation" category shows a distinct concentration of activations in the higher layers, suggesting that translation-related processing occurs later in the model.
* The "Linguistic" category has a broad distribution of activations, indicating its involvement across multiple layers and heads.
### Interpretation
The heatmaps suggest a hierarchical processing structure within the model. Earlier layers (0-18) seem to handle more general or "algorithmic" features, while later layers (18-30) are more specialized in processing "knowledge," "linguistic," and "translation" information. The concentration of "Translation" activations in the higher layers supports the idea that translation is a more complex process that builds upon lower-level linguistic representations. The varying levels of activation across categories and layers provide insights into the model's internal representations and how different types of information are processed. The "All Categories" heatmap serves as a baseline, showing the overall activity level, while the individual category heatmaps reveal the specific contributions of each category to the model's overall behavior. The presence of 4-category activations in specific regions suggests areas where multiple features or concepts are strongly represented, potentially indicating key processing nodes.
