\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 charts display accuracy as a function of layer number, with different lines representing different question-answering datasets and anchoring methods. ### Components/Axes * **X-axis:** Layer (ranging from approximately 0 to 30). * **Y-axis:** Answer Accuracy (ranging from 0 to 100). * **Left Chart Title:** Mistral-7B-v0.1 * **Right Chart Title:** Mistral-7B-v0.3 * **Legend:** Located at the bottom of the image, containing the following data series: * Q-Anchored (PopQA) - Blue solid line * Q-Anchored (TriviaQA) - Purple solid line * A-Anchored (PopQA) - Orange dashed line * A-Anchored (TriviaQA) - Green dashed line * Q-Anchored (HotpotQA) - Brown dashed-dotted line * A-Anchored (HotpotQA) - Light Blue dashed-dotted line * Q-Anchored (NQ) - Teal solid line * A-Anchored (NQ) - Red dashed line ### Detailed Analysis or Content Details **Mistral-7B-v0.1 (Left Chart):** * **Q-Anchored (PopQA):** Starts at approximately 90% accuracy, dips to around 30% at layer 2, then rises and plateaus around 85-95% from layer 8 onwards. * **Q-Anchored (TriviaQA):** Starts at approximately 90% accuracy, dips to around 40% at layer 2, then rises and plateaus around 80-90% from layer 8 onwards. * **A-Anchored (PopQA):** Starts at approximately 40% accuracy, remains relatively stable around 40-50% throughout all layers. * **A-Anchored (TriviaQA):** Starts at approximately 40% accuracy, remains relatively stable around 40-50% throughout all layers. * **Q-Anchored (HotpotQA):** Starts at approximately 90% accuracy, dips to around 30% at layer 2, then rises and plateaus around 80-90% from layer 8 onwards. * **A-Anchored (HotpotQA):** Starts at approximately 40% accuracy, remains relatively stable around 40-50% throughout all layers. * **Q-Anchored (NQ):** Starts at approximately 90% accuracy, dips to around 30% at layer 2, then rises and plateaus around 85-95% from layer 8 onwards. * **A-Anchored (NQ):** Starts at approximately 40% accuracy, remains relatively stable around 40-50% throughout all layers. **Mistral-7B-v0.3 (Right Chart):** * **Q-Anchored (PopQA):** Starts at approximately 95% accuracy, dips to around 35% at layer 2, then rises and plateaus around 90-100% from layer 8 onwards. * **Q-Anchored (TriviaQA):** Starts at approximately 95% accuracy, dips to around 45% at layer 2, then rises and plateaus around 85-95% from layer 8 onwards. * **A-Anchored (PopQA):** Starts at approximately 40% accuracy, remains relatively stable around 40-50% throughout all layers. * **A-Anchored (TriviaQA):** Starts at approximately 40% accuracy, remains relatively stable around 40-50% throughout all layers. * **Q-Anchored (HotpotQA):** Starts at approximately 95% accuracy, dips to around 35% at layer 2, then rises and plateaus around 85-95% from layer 8 onwards. * **A-Anchored (HotpotQA):** Starts at approximately 40% accuracy, remains relatively stable around 40-50% throughout all layers. * **Q-Anchored (NQ):** Starts at approximately 95% accuracy, dips to around 35% at layer 2, then rises and plateaus around 90-100% from layer 8 onwards. * **A-Anchored (NQ):** Starts at approximately 40% accuracy, remains relatively stable around 40-50% throughout all layers. ### Key Observations * All "Q-Anchored" lines exhibit a similar initial drop in accuracy at the beginning layers (0-2), followed by a recovery and plateauing at higher accuracy levels. * "A-Anchored" lines consistently show lower and more stable accuracy across all layers, remaining around 40-50%. * Mistral-7B-v0.3 generally achieves higher accuracy than Mistral-7B-v0.1 across all datasets and anchoring methods. * The accuracy difference between Q-Anchored and A-Anchored methods is significant, with Q-Anchored consistently outperforming A-Anchored. ### Interpretation The data suggests that the Mistral models, particularly v0.3, demonstrate improved performance with increasing layers, after an initial dip. The "Q-Anchored" approach consistently yields significantly higher accuracy than the "A-Anchored" approach, indicating that anchoring questions is more effective than anchoring answers for these question-answering tasks. The consistent low accuracy of A-Anchored methods suggests that this approach may not be well-suited for these datasets or model architecture. The higher accuracy of Mistral-7B-v0.3 compared to v0.1 indicates that the model improvements in version 0.3 have a positive impact on answer accuracy. The initial dip in accuracy across all Q-Anchored lines could be attributed to the model adapting to the specific layers or learning initial representations. The plateauing of accuracy at higher layers suggests that the model has reached a point of diminishing returns in terms of layer depth. The consistent performance across datasets for each anchoring method suggests that the anchoring strategy is more influential than the specific dataset.