## Dual Line Charts: Layer-wise ΔP for Mistral-7B-Instruct Models ### Overview The image displays two side-by-side line charts comparing the layer-wise performance change (ΔP) for two versions of the Mistral-7B-Instruct model (v0.1 and v0.3) across four different question-answering datasets. Each chart plots ΔP against the model layer number (0 to 30). The data is split into two primary conditions: "Q-Anchored" and "A-Anchored" for each dataset. ### Components/Axes * **Chart Titles:** * Left Chart: `Mistral-7B-Instruct-v0.1` * Right Chart: `Mistral-7B-Instruct-v0.3` * **X-Axis (Both Charts):** * Label: `Layer` * Scale: Linear, from 0 to 30, with major ticks at 0, 10, 20, 30. * **Y-Axis (Both Charts):** * Label: `ΔP` (Delta P) * Scale: Linear, from -80 to 0, with major ticks at -80, -60, -40, -20, 0. * **Legend (Positioned below both charts):** * Contains 8 entries, organized in two rows of four. * **Row 1 (Q-Anchored):** 1. `Q-Anchored (PopQA)` - Solid blue line. 2. `Q-Anchored (TriviaQA)` - Solid green line. 3. `Q-Anchored (HotpotQA)` - Dashed purple line. 4. `Q-Anchored (NQ)` - Solid pink line. * **Row 2 (A-Anchored):** 1. `A-Anchored (PopQA)` - Dashed orange line. 2. `A-Anchored (TriviaQA)` - Dashed red line. 3. `A-Anchored (HotpotQA)` - Dashed gray line. 4. `A-Anchored (NQ)` - Dashed light blue line. ### Detailed Analysis **Chart 1: Mistral-7B-Instruct-v0.1** * **Q-Anchored Series (All show a strong negative trend):** * **Trend:** All four lines begin near ΔP=0 at Layer 0 and exhibit a steep, roughly linear decline as layer number increases. * **PopQA (Blue, Solid):** Declines the most sharply. Approximate values: ~-40 at Layer 10, ~-65 at Layer 20, ending near -80 at Layer 30. * **TriviaQA (Green, Solid):** Follows a similar path to PopQA but is slightly less negative. Approximate values: ~-35 at Layer 10, ~-60 at Layer 20, ending near -70 at Layer 30. * **HotpotQA (Purple, Dashed):** Declines less steeply than PopQA/TriviaQA initially but converges with them at higher layers. Approximate values: ~-30 at Layer 10, ~-55 at Layer 20, ending near -65 at Layer 30. * **NQ (Pink, Solid):** Shows the least negative trend among the Q-Anchored group. Approximate values: ~-25 at Layer 10, ~-50 at Layer 20, ending near -60 at Layer 30. * **A-Anchored Series (All show minimal change, hovering near zero):** * **Trend:** All four dashed lines remain relatively flat, fluctuating closely around the ΔP=0 baseline across all layers. * **PopQA (Orange, Dashed):** Fluctuates between approximately +5 and -10. * **TriviaQA (Red, Dashed):** Fluctuates between approximately +5 and -10. * **HotpotQA (Gray, Dashed):** Fluctuates between approximately +5 and -5. * **NQ (Light Blue, Dashed):** Fluctuates between approximately +5 and -5. **Chart 2: Mistral-7B-Instruct-v0.3** * **Q-Anchored Series (Trends are very similar to v0.1, with slightly more negative endpoints for some):** * **Trend:** Consistent steep decline from Layer 0 to Layer 30. * **PopQA (Blue, Solid):** Approximate values: ~-45 at Layer 10, ~-70 at Layer 20, ending near -80 at Layer 30. * **TriviaQA (Green, Solid):** Approximate values: ~-40 at Layer 10, ~-65 at Layer 20, ending near -75 at Layer 30. * **HotpotQA (Purple, Dashed):** Approximate values: ~-35 at Layer 10, ~-60 at Layer 20, ending near -70 at Layer 30. * **NQ (Pink, Solid):** Approximate values: ~-30 at Layer 10, ~-55 at Layer 20, ending near -65 at Layer 30. * **A-Anchored Series (Consistently flat near zero, similar to v0.1):** * **Trend:** All lines show negligible change, staying within a narrow band around ΔP=0. * **PopQA (Orange, Dashed):** Fluctuates between approximately +5 and -10. * **TriviaQA (Red, Dashed):** Fluctuates between approximately +5 and -10. * **HotpotQA (Gray, Dashed):** Fluctuates between approximately +5 and -5. * **NQ (Light Blue, Dashed):** Fluctuates between approximately +5 and -5. ### Key Observations 1. **Dominant Pattern:** There is a stark, consistent dichotomy between the Q-Anchored and A-Anchored conditions across both model versions and all four datasets. 2. **Q-Anchored Degradation:** The Q-Anchored condition leads to a significant and progressive decrease in ΔP as the layer number increases, suggesting a cumulative negative effect through the model's layers. 3. **A-Anchored Stability:** The A-Anchored condition shows remarkable stability, with ΔP remaining near zero throughout all layers, indicating little to no layer-wise degradation. 4. **Dataset Hierarchy:** Within the Q-Anchored group, the magnitude of decline follows a consistent order across layers: PopQA (most negative) > TriviaQA > HotpotQA > NQ (least negative). 5. **Model Version Similarity:** The overall patterns and relationships between datasets are highly similar between Mistral-7B-Instruct-v0.1 and v0.3. The primary difference is that v0.3 shows slightly more negative ΔP values for the Q-Anchored series at equivalent layers. ### Interpretation The data strongly suggests that the method of "anchoring" (likely referring to how information is presented or retrieved during inference) has a profound impact on the internal layer-wise performance metric (ΔP) of these language models. * **Q-Anchored vs. A-Anchored:** The "Q-Anchored" approach (possibly anchoring on the Question) appears to cause a systematic degradation in the measured property (ΔP) as information propagates through the model's layers. In contrast, the "A-Anchored" approach (possibly anchoring on the Answer) maintains stability. This could imply that framing tasks around questions versus answers engages the model's internal processing in fundamentally different ways, with the question-centric approach leading to a form of cumulative "drift" or loss. * **Layer-wise Progression:** The near-linear decline for Q-Anchored series indicates the effect is not localized but propagates and compounds through the network depth. This is a critical insight for understanding how different prompting or retrieval strategies affect internal representations. * **Dataset Sensitivity:** The consistent hierarchy (PopQA > TriviaQA > HotpotQA > NQ) in the Q-Anchored decline suggests that the nature of the dataset (e.g., question complexity, answer specificity, knowledge type) modulates the severity of this layer-wise effect. PopQA, which shows the steepest decline, might represent a task type that is particularly disruptive under the Q-Anchored condition. * **Model Robustness:** The similarity between v0.1 and v0.3 indicates this is a stable characteristic of the model architecture or training paradigm, not an artifact of a specific version. The slightly worse performance in v0.3 for Q-Anchored tasks could hint at a trade-off introduced during model updates. In summary, this visualization provides clear evidence that the anchoring strategy is a major determinant of internal model dynamics, with question-anchored processing inducing a significant, layer-dependent negative shift in the ΔP metric across diverse knowledge-intensive tasks.