\n ## Line Charts: ΔP Across Layers for Llama-3.2 Models ### Overview The image displays two side-by-side line charts comparing the performance change (ΔP) across the layers of two different-sized language models: Llama-3.2-1B (left) and Llama-3.2-3B (right). Each chart plots multiple data series representing different experimental conditions (Q-Anchored vs. A-Anchored) across four question-answering datasets (PopQA, TriviaQA, HotpotQA, NQ). The charts illustrate how the measured metric ΔP evolves as information passes through the model's layers. ### Components/Axes * **Chart Titles:** * Left Chart: `Llama-3.2-1B` * Right Chart: `Llama-3.2-3B` * **X-Axis (Both Charts):** Labeled `Layer`. Represents the sequential layers of the neural network. * Llama-3.2-1B: Ticks at 0, 5, 10, 15. The axis spans approximately layers 0 to 16. * Llama-3.2-3B: Ticks at 0, 5, 10, 15, 20, 25. The axis spans approximately layers 0 to 26. * **Y-Axis (Both Charts):** Labeled `ΔP`. Represents a change in probability or performance metric. The scale ranges from approximately -80 to +10, with major ticks at -80, -60, -40, -20, 0. * **Legend (Bottom, spanning both charts):** Contains 8 entries, differentiating lines by color and style (solid vs. dashed). * **Q-Anchored (Solid Lines):** * `Q-Anchored (PopQA)`: Blue solid line. * `Q-Anchored (TriviaQA)`: Green solid line. * `Q-Anchored (HotpotQA)`: Purple solid line. * `Q-Anchored (NQ)`: Pink solid line. * **A-Anchored (Dashed Lines):** * `A-Anchored (PopQA)`: Orange dashed line. * `A-Anchored (TriviaQA)`: Red dashed line. * `A-Anchored (HotpotQA)`: Brown dashed line. * `A-Anchored (NQ)`: Gray dashed line. ### Detailed Analysis **Llama-3.2-1B Chart (Left):** * **Q-Anchored Series (Solid Lines):** All four solid lines show a strong, consistent downward trend. They start near ΔP = -10 to -20 at Layer 0 and decline steadily to between -60 and -80 by Layer 16. The lines are tightly clustered, with the green (TriviaQA) and blue (PopQA) lines often at the lower bound of the cluster. Shaded regions around each line indicate variance or confidence intervals. * **A-Anchored Series (Dashed Lines):** All four dashed lines remain relatively flat and close to ΔP = 0 across all layers. They exhibit minor fluctuations but no significant upward or downward trend. The orange (PopQA) and red (TriviaQA) dashed lines show slightly more volatility than the others, occasionally dipping to around -10. **Llama-3.2-3B Chart (Right):** * **Q-Anchored Series (Solid Lines):** The pattern is similar to the 1B model but extended over more layers. The downward trend begins at Layer 0 (ΔP ≈ -20) and continues to Layer 26, where values reach between -70 and -80. The decline appears slightly less linear than in the 1B model, with some minor plateaus and variations. The green (TriviaQA) line again often represents the lowest values. * **A-Anchored Series (Dashed Lines):** Consistent with the 1B model, these lines hover near ΔP = 0 throughout all 26 layers. They show minor noise but no directional trend. The orange (PopQA) dashed line is again the most volatile within this group. ### Key Observations 1. **Fundamental Dichotomy:** There is a stark, consistent difference between the behavior of Q-Anchored and A-Anchored conditions. Q-Anchored leads to a severe, layer-dependent degradation in ΔP, while A-Anchored maintains a stable ΔP near zero. 2. **Model Size Scaling:** The core trend is preserved when scaling from the 1B to the 3B parameter model. The 3B model simply extends the layer-wise analysis further, showing the Q-Anchored decline continues predictably with depth. 3. **Dataset Similarity:** Within each anchoring condition (Q or A), the four datasets (PopQA, TriviaQA, HotpotQA, NQ) produce remarkably similar trajectories. This suggests the observed effect is robust across different data sources and not an artifact of a specific dataset. 4. **Variance:** The shaded error bands are relatively narrow, indicating the reported trends are consistent across multiple runs or samples. ### Interpretation This visualization presents a clear technical finding about the internal dynamics of Llama-3.2 models during a specific task (likely related to question answering or knowledge recall). * **What the Data Suggests:** The metric ΔP, which likely measures the model's confidence or probability assigned to a correct answer, is highly sensitive to the "anchoring" method used during processing. "Q-Anchored" (possibly meaning the model's processing is conditioned heavily on the question) causes a catastrophic, layer-by-layer erosion of this confidence. In contrast, "A-Anchored" (possibly conditioning on the answer or a different representation) preserves the initial confidence level throughout the network's depth. * **Relationship Between Elements:** The charts demonstrate that this degradation is a function of network depth (layer number) and is intrinsic to the model architecture/training, as it manifests identically in both the 1B and 3B variants. The consistency across datasets reinforces that this is a general mechanistic property, not a data-specific quirk. * **Notable Implications:** The findings imply that for this task, the way information is "anchored" or represented as it flows through the model's layers is critical. The Q-Anchored pathway appears to suffer from a form of signal degradation or interference that accumulates with depth. This could inform techniques for improving model performance, such as modifying how question information is propagated or introducing architectural changes to stabilize representations in deeper layers. The stability of the A-Anchored condition provides a potential baseline or target for such interventions.