## Chart Type: Line Graphs Comparing Model Performance on Question Answering Tasks ### Overview The image contains two line graphs comparing the performance of two language models, Qwen3-8B and Qwen3-32B, on various question-answering tasks. The graphs plot the change in performance (ΔP) across different layers of the model. Each line represents a different question-answering dataset, with separate lines for question-anchored (Q-Anchored) and answer-anchored (A-Anchored) approaches. ### Components/Axes * **Titles:** * Left Graph: Qwen3-8B * Right Graph: Qwen3-32B * **X-axis:** Layer (Number of layers in the model) * Left Graph: Scale from 0 to 30, with ticks at approximately 0, 10, 20, and 30. * Right Graph: Scale from 0 to 60, with ticks at approximately 0, 20, 40, and 60. * **Y-axis:** ΔP (Change in Performance) * Scale from -80 to 0, with ticks at -80, -60, -40, -20, and 0. * **Legend:** Located at the bottom of the image, spanning both graphs. * **Q-Anchored (PopQA):** Solid blue line * **A-Anchored (PopQA):** Dashed brown line * **Q-Anchored (TriviaQA):** Dotted green line * **A-Anchored (TriviaQA):** Dotted-dashed grey line * **Q-Anchored (HotpotQA):** Solid light-green line * **A-Anchored (HotpotQA):** Dashed light-brown line * **Q-Anchored (NQ):** Dotted-dashed pink line * **A-Anchored (NQ):** Dotted-dashed grey line ### Detailed Analysis **Left Graph (Qwen3-8B):** * **Q-Anchored (PopQA):** (Solid Blue) Starts at approximately -20 and decreases to approximately -80 by layer 30. * **A-Anchored (PopQA):** (Dashed Brown) Remains relatively constant around 0 across all layers. * **Q-Anchored (TriviaQA):** (Dotted Green) Starts at approximately -20 and decreases to approximately -70 by layer 30. * **A-Anchored (TriviaQA):** (Dotted-Dashed Grey) Starts at approximately -20 and decreases to approximately -70 by layer 30. * **Q-Anchored (HotpotQA):** (Solid Light-Green) Starts at approximately -20 and decreases to approximately -70 by layer 30. * **A-Anchored (HotpotQA):** (Dashed Light-Brown) Remains relatively constant around 0 across all layers. * **Q-Anchored (NQ):** (Dotted-Dashed Pink) Starts at approximately -20 and decreases to approximately -70 by layer 30. * **A-Anchored (NQ):** (Dotted-Dashed Grey) Starts at approximately -20 and decreases to approximately -70 by layer 30. **Right Graph (Qwen3-32B):** * **Q-Anchored (PopQA):** (Solid Blue) Starts at approximately -20 and decreases to approximately -80 by layer 60. * **A-Anchored (PopQA):** (Dashed Brown) Remains relatively constant around 0 across all layers. * **Q-Anchored (TriviaQA):** (Dotted Green) Starts at approximately -20 and decreases to approximately -70 by layer 60. * **A-Anchored (TriviaQA):** (Dotted-Dashed Grey) Starts at approximately -20 and decreases to approximately -70 by layer 60. * **Q-Anchored (HotpotQA):** (Solid Light-Green) Starts at approximately -20 and decreases to approximately -70 by layer 60. * **A-Anchored (HotpotQA):** (Dashed Light-Brown) Remains relatively constant around 0 across all layers. * **Q-Anchored (NQ):** (Dotted-Dashed Pink) Starts at approximately -20 and decreases to approximately -70 by layer 60. * **A-Anchored (NQ):** (Dotted-Dashed Grey) Starts at approximately -20 and decreases to approximately -70 by layer 60. ### Key Observations * The performance (ΔP) of Q-Anchored methods generally decreases as the layer number increases for both models. * The performance (ΔP) of A-Anchored (PopQA) and A-Anchored (HotpotQA) methods remains relatively constant around 0 across all layers for both models. * The Qwen3-32B model has twice as many layers as the Qwen3-8B model (60 vs 30). * The trends in performance change are similar for both models across the different question-answering datasets. ### Interpretation The data suggests that increasing the number of layers in the Qwen3 models negatively impacts the performance of question-anchored methods on the tested question-answering tasks. The answer-anchored methods, specifically PopQA and HotpotQA, appear to be less sensitive to the number of layers. This could indicate that question-anchoring becomes more challenging as the model depth increases, possibly due to issues like vanishing gradients or increased complexity in processing the question. The A-Anchored methods are not impacted by the number of layers. The similarity in trends between the two models suggests that the observed behavior is consistent across different model sizes within the Qwen3 family. The shaded regions around the lines likely represent the standard deviation or confidence intervals, indicating the variability in performance across different runs or data samples.

## Line Chart: ΔP vs. Layer for Qwen Models ### Overview The image presents two line charts comparing the change in probability (ΔP) across layers for two Qwen language models: Qwen3-8B and Qwen3-32B. Each chart displays multiple lines representing different anchoring methods (Q-Anchored and A-Anchored) and datasets (PopQA, TriviaQA, HotpotQA, and NQ). The x-axis represents the layer number, and the y-axis represents ΔP. ### Components/Axes * **X-axis:** Layer (ranging from approximately 0 to 35 for Qwen3-8B and 0 to 60 for Qwen3-32B). * **Y-axis:** ΔP (ranging from approximately -90 to 0). * **Models:** Qwen3-8B (left chart), Qwen3-32B (right chart). * **Anchoring Methods:** Q-Anchored, A-Anchored. * **Datasets:** PopQA, TriviaQA, HotpotQA, NQ. * **Legend:** Located at the bottom of the image, associating colors with specific anchoring method/dataset combinations. ### Detailed Analysis or Content Details **Qwen3-8B Chart (Left):** * **Q-Anchored (PopQA):** (Dark Blue Line) Starts at approximately ΔP = 0 at Layer 0, rapidly decreases to approximately ΔP = -80 at Layer 10, and continues to decrease, reaching approximately ΔP = -85 at Layer 30, then slightly increases to approximately ΔP = -82 at Layer 35. * **A-Anchored (PopQA):** (Light Brown Line) Starts at approximately ΔP = 0 at Layer 0, decreases to approximately ΔP = -20 at Layer 5, then plateaus around ΔP = -20 to -30 for layers 5 to 35. * **Q-Anchored (TriviaQA):** (Medium Blue Line) Starts at approximately ΔP = 0 at Layer 0, decreases to approximately ΔP = -40 at Layer 5, continues to decrease to approximately ΔP = -70 at Layer 20, and reaches approximately ΔP = -75 at Layer 35. * **A-Anchored (TriviaQA):** (Light Purple Line) Starts at approximately ΔP = 0 at Layer 0, decreases to approximately ΔP = -20 at Layer 5, then plateaus around ΔP = -25 to -35 for layers 5 to 35. * **Q-Anchored (HotpotQA):** (Dark Purple Line) Starts at approximately ΔP = 0 at Layer 0, decreases to approximately ΔP = -30 at Layer 5, continues to decrease to approximately ΔP = -60 at Layer 20, and reaches approximately ΔP = -70 at Layer 35. * **A-Anchored (HotpotQA):** (Light Green Line) Starts at approximately ΔP = 0 at Layer 0, decreases to approximately ΔP = -20 at Layer 5, then plateaus around ΔP = -25 to -35 for layers 5 to 35. * **Q-Anchored (NQ):** (Teal Line) Starts at approximately ΔP = 0 at Layer 0, decreases to approximately ΔP = -30 at Layer 5, continues to decrease to approximately ΔP = -60 at Layer 20, and reaches approximately ΔP = -70 at Layer 35. * **A-Anchored (NQ):** (Orange Line) Starts at approximately ΔP = 0 at Layer 0, decreases to approximately ΔP = -20 at Layer 5, then plateaus around ΔP = -25 to -35 for layers 5 to 35. **Qwen3-32B Chart (Right):** * **Q-Anchored (PopQA):** (Dark Blue Line) Starts at approximately ΔP = 0 at Layer 0, rapidly decreases to approximately ΔP = -80 at Layer 10, and continues to decrease, reaching approximately ΔP = -85 at Layer 20, then slightly increases to approximately ΔP = -80 at Layer 60. * **A-Anchored (PopQA):** (Light Brown Line) Starts at approximately ΔP = 0 at Layer 0, decreases to approximately ΔP = -20 at Layer 5, then plateaus around ΔP = -20 to -30 for layers 5 to 60. * **Q-Anchored (TriviaQA):** (Medium Blue Line) Starts at approximately ΔP = 0 at Layer 0, decreases to approximately ΔP = -40 at Layer 5, continues to decrease to approximately ΔP = -70 at Layer 20, and reaches approximately ΔP = -75 at Layer 60. * **A-Anchored (TriviaQA):** (Light Purple Line) Starts at approximately ΔP = 0 at Layer 0, decreases to approximately ΔP = -20 at Layer 5, then plateaus around ΔP = -25 to -35 for layers 5 to 60. * **Q-Anchored (HotpotQA):** (Dark Purple Line) Starts at approximately ΔP = 0 at Layer 0, decreases to approximately ΔP = -30 at Layer 5, continues to decrease to approximately ΔP = -60 at Layer 20, and reaches approximately ΔP = -70 at Layer 60. * **A-Anchored (HotpotQA):** (Light Green Line) Starts at approximately ΔP = 0 at Layer 0, decreases to approximately ΔP = -20 at Layer 5, then plateaus around ΔP = -25 to -35 for layers 5 to 60. * **Q-Anchored (NQ):** (Teal Line) Starts at approximately ΔP = 0 at Layer 0, decreases to approximately ΔP = -30 at Layer 5, continues to decrease to approximately ΔP = -60 at Layer 20, and reaches approximately ΔP = -70 at Layer 60. * **A-Anchored (NQ):** (Orange Line) Starts at approximately ΔP = 0 at Layer 0, decreases to approximately ΔP = -20 at Layer 5, then plateaus around ΔP = -25 to -35 for layers 5 to 60. ### Key Observations * For both models, Q-Anchored lines consistently show a steeper decrease in ΔP compared to A-Anchored lines. * A-Anchored lines tend to plateau after a certain layer, indicating a stabilization of the probability change. * The datasets (PopQA, TriviaQA, HotpotQA, NQ) exhibit similar trends for both anchoring methods, but the magnitude of ΔP varies. * The Qwen3-32B model shows a similar trend to Qwen3-8B, but extends to a larger number of layers. ### Interpretation The charts demonstrate the impact of different anchoring methods and datasets on the change in probability across layers in Qwen language models. The steeper decline in ΔP for Q-Anchored lines suggests that this method leads to a more significant shift in the model's internal representations as it processes information through deeper layers. The plateauing of A-Anchored lines indicates that this method may result in more stable, but potentially less adaptable, representations. The consistent trends across datasets suggest that these observations are not specific to any particular type of question-answering task. The larger number of layers in Qwen3-32B allows for a more extended exploration of these trends, potentially revealing further insights into the model's behavior. The negative ΔP values indicate a decrease in probability, which could be interpreted as a reduction in confidence or a shift in the model's focus as it processes information.

## Line Charts: Qwen3-8B and Qwen3-32B Layer-wise ΔP Analysis ### Overview The image displays two side-by-side line charts comparing the layer-wise change in probability (ΔP) for two different-sized language models: Qwen3-8B (left) and Qwen3-32B (right). Each chart plots the ΔP metric across the model's layers for four different question-answering datasets, using two distinct anchoring methods (Q-Anchored and A-Anchored). ### Components/Axes * **Chart Titles:** "Qwen3-8B" (left chart), "Qwen3-32B" (right chart). * **X-Axis:** Labeled "Layer". Represents the sequential layers within the neural network model. * Qwen3-8B chart: Scale from 0 to 30, with major ticks at 0, 10, 20, 30. * Qwen3-32B chart: Scale from 0 to 60, with major ticks at 0, 20, 40, 60. * **Y-Axis:** Labeled "ΔP". Represents a change in probability metric. The scale is negative, ranging from 0 at the top to -80 at the bottom, with major ticks at 0, -20, -40, -60, -80. * **Legend:** Positioned at the bottom, spanning the width of both charts. It defines eight data series using a combination of color and line style: * **Solid Lines (Q-Anchored):** * Blue: Q-Anchored (PopQA) * Green: Q-Anchored (TriviaQA) * Purple: Q-Anchored (HotpotQA) * Pink: Q-Anchored (NQ) * **Dashed Lines (A-Anchored):** * Orange: A-Anchored (PopQA) * Red: A-Anchored (TriviaQA) * Gray: A-Anchored (HotpotQA) * Light Blue: A-Anchored (NQ) * **Data Series:** Each chart contains eight lines (four solid, four dashed) with shaded regions around them, likely indicating variance or confidence intervals. ### Detailed Analysis **Trend Verification & Data Point Extraction (Approximate):** * **A-Anchored Series (All Dashed Lines):** In both charts, all four A-Anchored lines (orange, red, gray, light blue) remain very close to ΔP = 0 across all layers. They exhibit minimal fluctuation, forming a nearly flat band at the top of the chart. This trend is consistent for both the 8B and 32B models. * **Q-Anchored Series (All Solid Lines):** All four Q-Anchored lines show a pronounced downward trend as layer number increases. * **Qwen3-8B Chart (Layers 0-30):** * The lines start near ΔP = 0 at Layer 0. * They descend steeply, reaching approximately ΔP = -60 to -70 by Layer 10. * The descent continues, albeit with more volatility, reaching a range of approximately ΔP = -70 to -85 by Layer 30. * The pink line (Q-Anchored (NQ)) appears to be the highest (least negative) among the Q-Anchored series in the later layers, while the blue line (Q-Anchored (PopQA)) is often the lowest (most negative). * **Qwen3-32B Chart (Layers 0-60):** * The lines start near ΔP = 0 at Layer 0. * They descend to approximately ΔP = -40 to -50 by Layer 20. * The downward trend continues, reaching approximately ΔP = -70 to -85 by Layer 60. * The pattern of the pink line (NQ) being relatively higher and the blue line (PopQA) being relatively lower among the Q-Anchored series is also visible here. **Spatial Grounding:** The legend is centered at the bottom. The Qwen3-8B chart occupies the left half of the image, and the Qwen3-32B chart occupies the right half. Within each chart, the A-Anchored lines are consistently positioned at the top (near y=0), while the Q-Anchored lines occupy the middle to bottom portion of the plot area, descending from top-left to bottom-right. ### Key Observations 1. **Anchoring Method Dominance:** The most striking pattern is the drastic difference between anchoring methods. A-Anchored processing results in negligible ΔP change across all layers, while Q-Anchored processing causes a large, layer-dependent decrease in ΔP. 2. **Layer-Dependent Effect:** For Q-Anchored methods, the ΔP metric is not static; it degrades progressively as information moves through the network layers. 3. **Model Scale Consistency:** The qualitative trends are remarkably consistent between the 8-billion-parameter and 32-billion-parameter models, suggesting the observed phenomenon is a property of the architecture or method, not model size. 4. **Dataset Variation:** While all Q-Anchored lines follow the same downward trend, there is consistent separation between datasets. The NQ dataset (pink) generally shows the smallest decrease, while PopQA (blue) often shows the largest decrease. ### Interpretation This visualization provides strong evidence that the choice of "anchoring" (likely referring to which part of the input—Question or Answer—is used as a reference point for some internal measurement) fundamentally alters how information is processed across the layers of these language models. * **A-Anchored Stability:** The flat lines for A-Anchored methods suggest that when the model's internal state is measured relative to the *Answer*, the metric ΔP remains stable. This could imply the answer representation is preserved or consistently referenced throughout the network. * **Q-Anchored Drift:** The steep decline for Q-Anchored methods indicates that when measured relative to the *Question*, the metric ΔP deteriorates. This suggests the model's internal representation progressively diverges from the initial question context as it processes information through deeper layers. The layer-wise progression implies this is a cumulative transformation. * **Practical Implication:** The findings highlight that model behavior and internal metrics are highly sensitive to the experimental setup (anchoring choice). Researchers must be precise in defining their measurement baselines. The consistency across model scales suggests this is a robust characteristic worth investigating for understanding how transformers process and transform query information. * **Dataset Sensitivity:** The consistent ordering of datasets (NQ > TriviaQA/HotpotQA > PopQA in terms of ΔP retention) might reflect differences in dataset complexity, question type, or how "answer-anchored" the model's training on those datasets was. PopQA, showing the largest drop, might contain questions that require the most significant transformation from the initial query to arrive at the answer.

## Line Graph: ΔP vs. Layer for GPT-3 Models (8B and 32B) ### Overview The image contains two side-by-side line graphs comparing the performance (ΔP) of different question-answering (QA) and answer-anchored (A-Anchored) models across layers in two GPT-3 variants: **Qwen3-8B** (left) and **Qwen3-32B** (right). The y-axis represents ΔP (change in performance), and the x-axis represents the layer number. Each graph includes multiple data series with distinct line styles and colors, as defined in the legend. --- ### Components/Axes - **X-Axis (Layer)**: - Labeled "Layer" for both subplots. - Ranges from 0 to 30 (8B) and 0 to 60 (32B). - **Y-Axis (ΔP)**: - Labeled "ΔP" for both subplots. - Ranges from -80 to 0. - **Legends**: - **Left Subplot (8B)**: - Solid blue: Q-Anchored (PopQA) - Dashed orange: A-Anchored (PopQA) - Dotted green: Q-Anchored (TriviaQA) - Dash-dot red: A-Anchored (TriviaQA) - Solid purple: Q-Anchored (HotpotQA) - Dashed pink: Q-Anchored (NQ) - **Right Subplot (32B)**: - Same legend as 8B, but with additional dashed pink line for Q-Anchored (NQ). --- ### Detailed Analysis #### Qwen3-8B (Left Subplot) 1. **Q-Anchored (PopQA)** (solid blue): - Starts at 0, drops sharply to ~-60 by layer 10, then fluctuates between -60 and -40. - Confidence interval (shaded area) widens slightly after layer 20. 2. **A-Anchored (PopQA)** (dashed orange): - Remains near 0 throughout, with minimal fluctuation. 3. **Q-Anchored (TriviaQA)** (dotted green): - Starts at ~-20, dips to ~-70 by layer 20, then stabilizes. 4. **A-Anchored (TriviaQA)** (dash-dot red): - Starts at ~-10, dips to ~-50 by layer 20, then stabilizes. 5. **Q-Anchored (HotpotQA)** (solid purple): - Starts at ~-10, dips to ~-50 by layer 20, then stabilizes. 6. **Q-Anchored (NQ)** (dashed pink): - Starts at ~-10, dips to ~-70 by layer 20, then fluctuates between -70 and -50. #### Qwen3-32B (Right Subplot) 1. **Q-Anchored (PopQA)** (solid blue): - Starts at 0, drops to ~-50 by layer 20, then stabilizes. 2. **A-Anchored (PopQA)** (dashed orange): - Remains near 0 throughout. 3. **Q-Anchored (TriviaQA)** (dotted green): - Starts at ~-30, dips to ~-70 by layer 40, then stabilizes. 4. **A-Anchored (TriviaQA)** (dash-dot red): - Starts at ~-20, dips to ~-60 by layer 40, then stabilizes. 5. **Q-Anchored (HotpotQA)** (solid purple): - Starts at ~-20, dips to ~-60 by layer 40, then stabilizes. 6. **Q-Anchored (NQ)** (dashed pink): - Starts at ~-10, dips to ~-80 by layer 60, then fluctuates between -80 and -60. --- ### Key Observations 1. **Stability of A-Anchored Models**: - A-Anchored models (PopQA, TriviaQA, HotpotQA) consistently show minimal ΔP changes, remaining near 0 across layers. 2. **Volatility of Q-Anchored Models**: - Q-Anchored models exhibit significant ΔP fluctuations, especially for NQ (Question-Answering) tasks. 3. **Layer-Specific Trends**: - Layers 10–20 (8B) and 20–40 (32B) show the most pronounced performance drops for Q-Anchored models. 4. **Confidence Intervals**: - Shaded areas around lines indicate uncertainty, which increases for Q-Anchored models in deeper layers. --- ### Interpretation - **Anchoring Method Impact**: - A-Anchored models (answer-focused) demonstrate stability, suggesting they are less sensitive to layer-specific variations. - Q-Anchored models (question-focused) show higher variability, possibly due to the complexity of question-answering tasks. - **Model Size Effects**: - The 32B model exhibits more pronounced fluctuations than the 8B model, indicating that larger models may amplify the impact of anchoring methods. - **NQ Task Challenges**: - The Q-Anchored (NQ) line in both subplots shows the most erratic behavior, highlighting difficulties in handling open-ended questions. - **Confidence Intervals**: - Wider shaded regions for Q-Anchored models suggest greater uncertainty in performance measurements, particularly in deeper layers. --- ### Spatial Grounding - **Legends**: Positioned at the bottom of each subplot, with clear color/style mappings. - **Data Series**: Lines are plotted directly above their corresponding legend entries, with no overlap in color/style. - **Axis Alignment**: Both subplots share identical axis labels and scales, enabling direct comparison. --- ### Content Details - **Numerical Approximations**: - ΔP values are estimated from the graph's scale (e.g., ~-60, ~-70) with ±5 uncertainty due to visual estimation. - Layer numbers are exact (0–30 for 8B, 0–60 for 32B). - **Text Embedding**: No additional text is present in the diagram beyond axis labels and legends. --- ### Final Notes The graph emphasizes the trade-off between anchoring methods and model performance stability. A-Anchored models prioritize consistency, while Q-Anchored models trade stability for potential gains in specific tasks. The 32B model's increased layer count amplifies these trends, suggesting architectural complexity influences anchoring effectiveness.