## Line Chart: Mistral-7B Model Performance Comparison ### Overview The image presents two line charts comparing the performance of Mistral-7B models (v0.1 and v0.3) across different layers. The charts depict the "Answer Accuracy" on the y-axis versus "Layer" on the x-axis for various question-answering datasets. Each dataset is represented by two lines: one for "Q-Anchored" (question-anchored) and one for "A-Anchored" (answer-anchored) approaches. ### Components/Axes * **Titles:** * Left Chart: "Mistral-7B-v0.1" * Right Chart: "Mistral-7B-v0.3" * **Y-Axis:** "Answer Accuracy", ranging from 0 to 100. Increments of 20. * **X-Axis:** "Layer", ranging from 0 to 30. Increments of 10. * **Legend:** Located at the bottom of the image, describing the lines: * Blue solid line: "Q-Anchored (PopQA)" * Brown dashed line: "A-Anchored (PopQA)" * Green dotted line: "Q-Anchored (TriviaQA)" * Red dashed-dotted line: "A-Anchored (TriviaQA)" * Purple dashed line: "Q-Anchored (HotpotQA)" * Orange dotted line: "A-Anchored (HotpotQA)" * Pink dashed-dotted line: "Q-Anchored (NQ)" * Gray dotted line: "A-Anchored (NQ)" ### Detailed Analysis **Left Chart: Mistral-7B-v0.1** * **Q-Anchored (PopQA) (Blue solid line):** Starts at approximately 0% accuracy, rises sharply to around 90% by layer 10, fluctuates between 80% and 100% until layer 30. * Specific points: (0, ~0), (10, ~90), (30, ~90) * **A-Anchored (PopQA) (Brown dashed line):** Starts around 50%, decreases to 30% by layer 5, then gradually increases to around 40% and remains relatively stable with fluctuations. * Specific points: (0, ~50), (5, ~30), (30, ~40) * **Q-Anchored (TriviaQA) (Green dotted line):** Starts around 60%, fluctuates between 80% and 100% throughout all layers. * Specific points: (0, ~60), (10, ~90), (30, ~90) * **A-Anchored (TriviaQA) (Red dashed-dotted line):** Starts around 50%, decreases to 20% by layer 10, then remains relatively stable with fluctuations. * Specific points: (0, ~50), (10, ~20), (30, ~20) * **Q-Anchored (HotpotQA) (Purple dashed line):** Starts around 60%, fluctuates between 80% and 100% throughout all layers. * Specific points: (0, ~60), (10, ~90), (30, ~90) * **A-Anchored (HotpotQA) (Orange dotted line):** Starts around 50%, decreases to 40% by layer 5, then remains relatively stable with fluctuations. * Specific points: (0, ~50), (5, ~40), (30, ~40) * **Q-Anchored (NQ) (Pink dashed-dotted line):** Starts around 60%, fluctuates between 80% and 100% throughout all layers. * Specific points: (0, ~60), (10, ~90), (30, ~90) * **A-Anchored (NQ) (Gray dotted line):** Starts around 50%, decreases to 20% by layer 10, then remains relatively stable with fluctuations. * Specific points: (0, ~50), (10, ~20), (30, ~20) **Right Chart: Mistral-7B-v0.3** * **Q-Anchored (PopQA) (Blue solid line):** Starts at approximately 0% accuracy, rises sharply to around 90% by layer 10, fluctuates between 70% and 100% until layer 30. * Specific points: (0, ~0), (10, ~90), (30, ~80) * **A-Anchored (PopQA) (Brown dashed line):** Starts around 50%, decreases to 30% by layer 5, then gradually increases to around 40% and remains relatively stable with fluctuations. * Specific points: (0, ~50), (5, ~30), (30, ~40) * **Q-Anchored (TriviaQA) (Green dotted line):** Starts around 60%, fluctuates between 80% and 100% throughout all layers. * Specific points: (0, ~60), (10, ~90), (30, ~90) * **A-Anchored (TriviaQA) (Red dashed-dotted line):** Starts around 50%, decreases to 20% by layer 10, then remains relatively stable with fluctuations. * Specific points: (0, ~50), (10, ~20), (30, ~20) * **Q-Anchored (HotpotQA) (Purple dashed line):** Starts around 60%, fluctuates between 80% and 100% throughout all layers. * Specific points: (0, ~60), (10, ~90), (30, ~90) * **A-Anchored (HotpotQA) (Orange dotted line):** Starts around 50%, decreases to 40% by layer 5, then remains relatively stable with fluctuations. * Specific points: (0, ~50), (5, ~40), (30, ~40) * **Q-Anchored (NQ) (Pink dashed-dotted line):** Starts around 60%, fluctuates between 80% and 100% throughout all layers. * Specific points: (0, ~60), (10, ~90), (30, ~90) * **A-Anchored (NQ) (Gray dotted line):** Starts around 50%, decreases to 20% by layer 10, then remains relatively stable with fluctuations. * Specific points: (0, ~50), (10, ~20), (30, ~20) ### Key Observations * **Q-Anchored vs. A-Anchored:** Q-Anchored approaches generally achieve significantly higher answer accuracy than A-Anchored approaches across all datasets and both model versions. * **Dataset Performance:** The Q-Anchored methods for TriviaQA, HotpotQA, and NQ datasets consistently achieve high accuracy (80-100%) across all layers. PopQA starts low and increases. * **Model Version Comparison:** The performance between Mistral-7B-v0.1 and Mistral-7B-v0.3 is very similar across all datasets and anchoring methods. * **Layer Impact:** For Q-Anchored methods, accuracy tends to stabilize after the initial layers (around layer 10). A-Anchored methods show relatively stable, lower accuracy across all layers. ### Interpretation The charts demonstrate the performance of Mistral-7B models on various question-answering datasets, highlighting the difference between question-anchored and answer-anchored approaches. The consistently higher accuracy of Q-Anchored methods suggests that focusing on the question context is more effective for these models. The similarity in performance between v0.1 and v0.3 indicates that the model's core capabilities remained consistent between these versions. The stabilization of accuracy after the initial layers suggests that the model learns the relevant information early on and maintains it throughout the subsequent layers. The A-Anchored methods show a consistent, lower accuracy, indicating that relying solely on the answer context is less effective for these models.

\n ## Line Chart: Answer Accuracy vs. Layer for Mistral Models ### Overview This image presents two line charts side-by-side, comparing the answer accuracy of the Mistral-7B-v0.1 and Mistral-7B-v0.3 models across different layers. The x-axis represents the layer number (from 0 to 30), and the y-axis represents the answer accuracy (from 0 to 100). Each chart displays multiple lines, each representing a different question-answering dataset and anchoring method. ### Components/Axes * **X-axis:** Layer (0 to 30, with increments of approximately 2-3) * **Y-axis:** Answer Accuracy (0 to 100, with increments of 10) * **Left Chart Title:** Mistral-7B-v0.1 * **Right Chart Title:** Mistral-7B-v0.3 * **Legend (Bottom):** * Blue Solid Line: Q-Anchored (PopQA) * Orange Dotted Line: A-Anchored (PopQA) * Green Solid Line: Q-Anchored (TriviaQA) * Red Dotted Line: A-Anchored (TriviaQA) * Purple Dashed Line: Q-Anchored (HotpotQA) * Teal Dashed Line: A-Anchored (HotpotQA) * Gray Solid Line: Q-Anchored (NQ) * Brown Dotted Line: A-Anchored (NQ) ### Detailed Analysis or Content Details **Mistral-7B-v0.1 (Left Chart):** * **Q-Anchored (PopQA) - Blue Solid Line:** Starts at approximately 0% accuracy at layer 0, rises sharply to around 80-90% by layer 5, then fluctuates between 70-95% for the remainder of the layers. * **A-Anchored (PopQA) - Orange Dotted Line:** Starts at approximately 0% accuracy at layer 0, rises to around 40-50% by layer 5, and remains relatively stable between 30-60% for the rest of the layers. * **Q-Anchored (TriviaQA) - Green Solid Line:** Starts at approximately 0% accuracy at layer 0, rises to around 80-90% by layer 5, and fluctuates between 70-95% for the remainder of the layers. * **A-Anchored (TriviaQA) - Red Dotted Line:** Starts at approximately 0% accuracy at layer 0, rises to around 40-50% by layer 5, and remains relatively stable between 30-60% for the rest of the layers. * **Q-Anchored (HotpotQA) - Purple Dashed Line:** Starts at approximately 0% accuracy at layer 0, rises to around 80-90% by layer 5, and fluctuates between 70-95% for the remainder of the layers. * **A-Anchored (HotpotQA) - Teal Dashed Line:** Starts at approximately 0% accuracy at layer 0, rises to around 40-50% by layer 5, and remains relatively stable between 30-60% for the rest of the layers. * **Q-Anchored (NQ) - Gray Solid Line:** Starts at approximately 0% accuracy at layer 0, rises to around 80-90% by layer 5, and fluctuates between 70-95% for the remainder of the layers. * **A-Anchored (NQ) - Brown Dotted Line:** Starts at approximately 0% accuracy at layer 0, rises to around 40-50% by layer 5, and remains relatively stable between 30-60% for the rest of the layers. **Mistral-7B-v0.3 (Right Chart):** * **Q-Anchored (PopQA) - Blue Solid Line:** Starts at approximately 0% accuracy at layer 0, rises sharply to around 80-90% by layer 5, then fluctuates between 70-95% for the remainder of the layers. * **A-Anchored (PopQA) - Orange Dotted Line:** Starts at approximately 0% accuracy at layer 0, rises to around 40-50% by layer 5, and remains relatively stable between 30-60% for the rest of the layers. * **Q-Anchored (TriviaQA) - Green Solid Line:** Starts at approximately 0% accuracy at layer 0, rises to around 80-90% by layer 5, and fluctuates between 70-95% for the remainder of the layers. * **A-Anchored (TriviaQA) - Red Dotted Line:** Starts at approximately 0% accuracy at layer 0, rises to around 40-50% by layer 5, and remains relatively stable between 30-60% for the rest of the layers. * **Q-Anchored (HotpotQA) - Purple Dashed Line:** Starts at approximately 0% accuracy at layer 0, rises to around 80-90% by layer 5, and fluctuates between 70-95% for the remainder of the layers. * **A-Anchored (HotpotQA) - Teal Dashed Line:** Starts at approximately 0% accuracy at layer 0, rises to around 40-50% by layer 5, and remains relatively stable between 30-60% for the rest of the layers. * **Q-Anchored (NQ) - Gray Solid Line:** Starts at approximately 0% accuracy at layer 0, rises to around 80-90% by layer 5, and fluctuates between 70-95% for the remainder of the layers. * **A-Anchored (NQ) - Brown Dotted Line:** Starts at approximately 0% accuracy at layer 0, rises to around 40-50% by layer 5, and remains relatively stable between 30-60% for the rest of the layers. ### Key Observations * The Q-Anchored lines consistently achieve significantly higher accuracy than the A-Anchored lines across all datasets and for both models. * Accuracy generally increases rapidly in the initial layers (0-5) and then plateaus with some fluctuations. * The two models (v0.1 and v0.3) exhibit very similar performance patterns. * The accuracy ranges for the Q-Anchored lines are similar across different datasets (PopQA, TriviaQA, HotpotQA, NQ). * The accuracy ranges for the A-Anchored lines are similar across different datasets (PopQA, TriviaQA, HotpotQA, NQ). ### Interpretation The data suggests that question-anchoring (Q-Anchored) is a more effective method for improving answer accuracy in the Mistral models compared to answer-anchoring (A-Anchored). Both models demonstrate a similar learning curve, with a rapid increase in accuracy in the early layers followed by a stabilization. The consistent performance across different datasets indicates that the observed trends are not specific to any particular question-answering task. The relatively low accuracy of the A-Anchored lines suggests that the models may struggle to effectively utilize answer-based information for improving performance. The fluctuations in accuracy after layer 5 could be due to overfitting or the inherent complexity of the question-answering tasks. The similarity between the two model versions (v0.1 and v0.3) suggests that the core architecture and training data are similar, and the improvements in v0.3 may not be substantial in terms of the overall accuracy trend.

## Line Charts: Mistral-7B Model Layer-wise Answer Accuracy ### Overview The image displays two side-by-side line charts comparing the "Answer Accuracy" across model layers (0-30) for two versions of the Mistral-7B model: v0.1 (left) and v0.3 (right). Each chart plots the performance of eight different evaluation setups, defined by a combination of an anchoring method (Q-Anchored or A-Anchored) and a dataset (PopQA, TriviaQA, HotpotQA, NQ). ### Components/Axes * **Titles:** * Left Chart: `Mistral-7B-v0.1` * Right Chart: `Mistral-7B-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: `Answer Accuracy`. Scale: Linear, from 0 to 100, with major ticks at 0, 20, 40, 60, 80, 100. * **Legend (Bottom, spanning both charts):** Contains 8 entries, each with a specific color and line style: 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)`: Dotted pink line. 5. `A-Anchored (PopQA)`: Dashed orange line. 6. `A-Anchored (TriviaQA)`: Dotted red line. 7. `A-Anchored (HotpotQA)`: Dash-dot gray line. 8. `A-Anchored (NQ)`: Dash-dot-dot light blue line. * **Plot Elements:** Each data series is represented by a colored line with a semi-transparent shaded band around it, likely indicating variance or confidence intervals. ### Detailed Analysis **Mistral-7B-v0.1 (Left Chart):** * **Q-Anchored Series (Generally Higher Accuracy):** * `Q-Anchored (TriviaQA)` (Solid Green): Shows a strong upward trend from layer 0, peaks near 100% accuracy between layers ~10-20, then gradually declines but remains above 80% at layer 30. * `Q-Anchored (HotpotQA)` (Dashed Purple): Follows a similar but slightly lower trajectory than TriviaQA, peaking near 100% around layer 15 and ending near 80%. * `Q-Anchored (PopQA)` (Solid Blue): Highly volatile. Starts low, spikes to ~90% near layer 5, drops sharply, then oscillates with high amplitude between ~20% and 90% across the remaining layers. * `Q-Anchored (NQ)` (Dotted Pink): Rises to a peak of ~95% around layer 10, then declines steadily to about 60% by layer 30. * **A-Anchored Series (Generally Lower, More Volatile Accuracy):** * All four A-Anchored lines (`PopQA`-orange, `TriviaQA`-red, `HotpotQA`-gray, `NQ`-light blue) cluster in the lower half of the chart (mostly between 20% and 60%). * They exhibit significant volatility and overlap, with no single dataset clearly dominating. Their trends are less defined, often dipping below 20% at various layers. **Mistral-7B-v0.3 (Right Chart):** * **Q-Anchored Series:** * `Q-Anchored (TriviaQA)` (Solid Green): Maintains very high accuracy (>90%) across almost all layers from 5 to 30, showing more stability than in v0.1. * `Q-Anchored (HotpotQA)` (Dashed Purple): Also shows improved stability, staying mostly above 80% after layer 5, with a dip around layer 20. * `Q-Anchored (PopQA)` (Solid Blue): Remains highly volatile, with sharp peaks and troughs across the entire layer range, similar to v0.1. * `Q-Anchored (NQ)` (Dotted Pink): Peaks early (~95% at layer 5) and then shows a more pronounced decline compared to v0.1, falling to around 50% by layer 30. * **A-Anchored Series:** * The cluster of A-Anchored lines remains in the lower accuracy band (20%-60%). * They appear slightly more separated than in v0.1, with `A-Anchored (PopQA)` (orange) and `A-Anchored (TriviaQA)` (red) showing somewhat more distinct, though still volatile, paths. ### Key Observations 1. **Anchoring Method Dominance:** The most striking pattern is the clear performance gap between Q-Anchored and A-Anchored methods. Q-Anchored approaches consistently achieve higher answer accuracy across both model versions and most datasets. 2. **Dataset Sensitivity:** Performance is highly dataset-dependent. `TriviaQA` and `HotpotQA` under Q-Anchoring show the most robust and high accuracy, especially in later layers of v0.3. `PopQA` under Q-Anchoring is uniquely unstable. 3. **Model Version Evolution (v0.1 to v0.3):** The transition from v0.1 to v0.3 appears to stabilize and improve the performance of the top-performing Q-Anchored series (`TriviaQA`, `HotpotQA`), particularly in the middle-to-late layers (10-30). The volatile `Q-Anchored (PopQA)` and declining `Q-Anchored (NQ)` patterns persist. 4. **Layer-wise Trends:** Accuracy is not monotonic with layer depth. For high-performing series, accuracy often peaks in the middle layers (5-20) before plateauing or declining. Early layers (0-5) generally show lower accuracy. ### Interpretation This data suggests that the **choice of anchoring method (Q vs. A) is a more critical factor for performance than the specific model version (v0.1 vs. v0.3)** for these tasks. Q-Anchoring, which likely conditions the model on the question, provides a much stronger signal for retrieving accurate answers than A-Anchoring (conditioning on the answer), which leads to noisy and poor performance. The **improvement from v0.1 to v0.3** indicates targeted refinement. The model's internal representations for factual recall (as measured by TriviaQA and HotpotQA) have become more robust and consistent across its depth, suggesting better knowledge consolidation or more effective information flow in the later version. The **extreme volatility of `Q-Anchored (PopQA)`** is a notable anomaly. It implies that for this specific dataset, the model's ability to produce accurate answers is highly sensitive to the specific layer being probed, possibly due to the nature of the questions or answers in PopQA interfering with the model's processing pathway. The **declining trend for `Q-Anchored (NQ)`** in both versions, but more sharply in v0.3, is curious. It might indicate that for Natural Questions, the most relevant information is encoded in middle layers, and deeper layers may be over-specializing or drifting away from this specific type of factual recall. In summary, the charts reveal that optimal performance is achieved by **combining Q-Anchoring with datasets like TriviaQA or HotpotQA and utilizing the model's middle-to-late layers**, with the newer model version offering more stability. The results highlight the importance of both the evaluation methodology (anchoring) and the model's internal layer-wise organization for factual accuracy.

## Line Graph: Answer Accuracy Across Layers for Mistral-7B Models ### Overview The image contains two side-by-side line graphs comparing answer accuracy across 30 layers of the Mistral-7B model (versions v0.1 and v0.3). Each graph plots answer accuracy (0–100%) against layer numbers (0–30). The data is segmented by QA datasets (PopQA, TriviaQA, HotpotQA, NQ) and anchoring methods (Q-Anchored vs. A-Anchored). --- ### Components/Axes - **X-axis**: "Layer" (0–30), representing model layers. - **Y-axis**: "Answer Accuracy" (0–100%), with gridlines at 20, 40, 60, 80, 100. - **Legends**: - **Left Chart (v0.1)**: - Solid blue: Q-Anchored (PopQA) - Dashed orange: A-Anchored (PopQA) - Solid green: Q-Anchored (TriviaQA) - Dashed red: A-Anchored (TriviaQA) - Solid purple: Q-Anchored (HotpotQA) - Dashed gray: A-Anchored (HotpotQA) - Solid pink: Q-Anchored (NQ) - Dashed black: A-Anchored (NQ) - **Right Chart (v0.3)**: Same legend as left chart. --- ### Detailed Analysis #### Left Chart (Mistral-7B-v0.1) - **Q-Anchored (PopQA)**: Starts at ~80% accuracy, dips to ~40% at layer 10, then fluctuates between 50–70%. - **A-Anchored (PopQA)**: Begins at ~30%, peaks at ~60% at layer 10, then drops to ~20% by layer 30. - **Q-Anchored (TriviaQA)**: Starts at ~70%, dips to ~30% at layer 10, then rises to ~60% by layer 30. - **A-Anchored (TriviaQA)**: Begins at ~20%, peaks at ~50% at layer 10, then declines to ~10% by layer 30. - **Q-Anchored (HotpotQA)**: Starts at ~75%, dips to ~40% at layer 10, then stabilizes at ~60%. - **A-Anchored (HotpotQA)**: Begins at ~25%, peaks at ~55% at layer 10, then drops to ~20%. - **Q-Anchored (NQ)**: Highly erratic, with sharp drops (e.g., ~90% → ~10% at layer 5) and peaks (e.g., ~80% at layer 20). - **A-Anchored (NQ)**: Smoother than Q-Anchored, with a peak of ~40% at layer 10 and a decline to ~20% by layer 30. #### Right Chart (Mistral-7B-v0.3) - **Q-Anchored (PopQA)**: Starts at ~85%, dips to ~45% at layer 10, then fluctuates between 50–75%. - **A-Anchored (PopQA)**: Begins at ~35%, peaks at ~65% at layer 10, then drops to ~25%. - **Q-Anchored (TriviaQA)**: Starts at ~75%, dips to ~35% at layer 10, then rises to ~65% by layer 30. - **A-Anchored (TriviaQA)**: Begins at ~25%, peaks at ~55% at layer 10, then declines to ~15%. - **Q-Anchored (HotpotQA)**: Starts at ~80%, dips to ~45% at layer 10, then stabilizes at ~70%. - **A-Anchored (HotpotQA)**: Begins at ~30%, peaks at ~60% at layer 10, then drops to ~25%. - **Q-Anchored (NQ)**: Similar erratic pattern to v0.1, with a sharp drop to ~10% at layer 5 and a peak of ~85% at layer 20. - **A-Anchored (NQ)**: Smoother than Q-Anchored, with a peak of ~45% at layer 10 and a decline to ~25%. --- ### Key Observations 1. **Q-Anchored vs. A-Anchored**: - Q-Anchored methods generally show higher peak accuracy but greater volatility (e.g., NQ dataset drops from ~90% to ~10% in v0.1). - A-Anchored methods are more stable but consistently lower in accuracy (e.g., A-Anchored (PopQA) peaks at ~60% vs. Q-Anchored’s ~80%). 2. **Model Version Differences**: - v0.3 shows slightly higher baseline accuracy for Q-Anchored methods (e.g., PopQA starts at ~85% vs. v0.1’s ~80%). - A-Anchored methods in v0.3 have marginally higher peaks (e.g., A-Anchored (PopQA) peaks at ~65% vs. v0.1’s ~60%). 3. **NQ Dataset Anomalies**: - Q-Anchored (NQ) exhibits extreme fluctuations, suggesting instability in handling this dataset. - A-Anchored (NQ) is less volatile but still underperforms compared to other datasets. --- ### Interpretation The data suggests that **Q-Anchored methods** (e.g., PopQA, TriviaQA) achieve higher accuracy in specific layers but are prone to instability, particularly with the NQ dataset. **A-Anchored methods** offer more consistent performance but lower overall accuracy. The slight improvements in v0.3 (e.g., higher baseline accuracy for Q-Anchored) indicate minor optimizations in the model architecture. The NQ dataset’s erratic behavior highlights challenges in generalizing across diverse QA tasks. **Notable Trends**: - Peaks in accuracy for Q-Anchored methods often occur around layer 10, suggesting early layers are critical for certain tasks. - A-Anchored methods show a "peak-and-decline" pattern, possibly due to overfitting or layer-specific limitations. This analysis underscores the trade-off between accuracy and stability in model design, with anchoring methods playing a pivotal role in performance.