## Line Chart: I-Don't-Know Rate Comparison for Mistral-7B-v0.1 and Mistral-7B-v0.3 ### Overview The image presents two line charts comparing the "I-Don't-Know Rate" across different layers (0-32) of the Mistral-7B-v0.1 and Mistral-7B-v0.3 models. Each chart displays multiple data series, representing different question-answering datasets (PopQA, TriviaQA, HotpotQA, and NQ) anchored by either the question (Q-Anchored) or the answer (A-Anchored). The charts aim to illustrate how the model's uncertainty varies across layers and datasets for the two model versions. ### Components/Axes * **Titles:** * Left Chart: "Mistral-7B-v0.1" * Right Chart: "Mistral-7B-v0.3" * **Y-Axis:** * Label: "I-Don't-Know Rate" * Scale: 0 to 100, with tick marks at 0, 20, 40, 60, 80, and 100. * **X-Axis:** * Label: "Layer" * Scale: 0 to approximately 32, with tick marks every 5 units (0, 10, 20, 30). * **Legend:** Located at the bottom of the image, it identifies each data series by color and line style: * Blue solid line: Q-Anchored (PopQA) * Brown dashed line: A-Anchored (PopQA) * Green dotted line: Q-Anchored (TriviaQA) * Orange dash-dot line: A-Anchored (TriviaQA) * Red dashed line: Q-Anchored (HotpotQA) * Gray dotted line: A-Anchored (HotpotQA) * Purple dash-dot line: Q-Anchored (NQ) * Black dashed line: A-Anchored (NQ) ### Detailed Analysis **Left Chart: Mistral-7B-v0.1** * **Q-Anchored (PopQA) (Blue solid line):** Starts at 100, drops sharply to near 0 by layer 5, then fluctuates between approximately 5 and 20 for the remaining layers. * **A-Anchored (PopQA) (Brown dashed line):** Starts around 50, rises to approximately 60-70, and remains relatively stable with minor fluctuations. * **Q-Anchored (TriviaQA) (Green dotted line):** Starts at 100, drops sharply to approximately 10-20 by layer 10, then fluctuates between 10 and 30. * **A-Anchored (TriviaQA) (Orange dash-dot line):** Starts around 50, rises to approximately 70-80, and remains relatively stable with minor fluctuations. * **Q-Anchored (HotpotQA) (Red dashed line):** Starts around 50, rises to approximately 70-80, and remains relatively stable with minor fluctuations. * **A-Anchored (HotpotQA) (Gray dotted line):** Starts around 60, remains relatively stable with minor fluctuations between 60 and 80. * **Q-Anchored (NQ) (Purple dash-dot line):** Starts around 40, fluctuates significantly between 10 and 40 across the layers. * **A-Anchored (NQ) (Black dashed line):** Starts around 60, remains relatively stable with minor fluctuations between 60 and 80. **Right Chart: Mistral-7B-v0.3** * **Q-Anchored (PopQA) (Blue solid line):** Starts at 100, drops sharply to approximately 10-20 by layer 10, then remains relatively stable with minor fluctuations. * **A-Anchored (PopQA) (Brown dashed line):** Starts around 70, remains relatively stable with minor fluctuations between 60 and 80. * **Q-Anchored (TriviaQA) (Green dotted line):** Starts at 100, drops sharply to approximately 20-30 by layer 5, then fluctuates between 20 and 40. * **A-Anchored (TriviaQA) (Orange dash-dot line):** Starts around 60, rises to approximately 70-80, and remains relatively stable with minor fluctuations. * **Q-Anchored (HotpotQA) (Red dashed line):** Starts around 60, rises to approximately 80-90, and remains relatively stable with minor fluctuations. * **A-Anchored (HotpotQA) (Gray dotted line):** Starts around 80, remains relatively stable with minor fluctuations between 70 and 90. * **Q-Anchored (NQ) (Purple dash-dot line):** Starts around 60, fluctuates significantly between 20 and 60 across the layers. * **A-Anchored (NQ) (Black dashed line):** Starts around 80, remains relatively stable with minor fluctuations between 70 and 90. ### Key Observations * For both models, the "Q-Anchored (PopQA)" series shows a significant drop in the "I-Don't-Know Rate" after the initial layers. * The "A-Anchored" series generally exhibit more stable "I-Don't-Know Rates" compared to the "Q-Anchored" series. * The Mistral-7B-v0.3 model appears to have a generally lower "I-Don't-Know Rate" for the "Q-Anchored (PopQA)" series after the initial layers compared to Mistral-7B-v0.1. * The shaded regions around each line indicate the confidence interval or standard deviation, showing the variability in the "I-Don't-Know Rate" across different runs or samples. ### Interpretation The charts provide insights into how the Mistral-7B models handle uncertainty across different layers and question-answering datasets. The "I-Don't-Know Rate" can be interpreted as a measure of the model's confidence in its predictions. The observed trends suggest that: * **Question Anchoring vs. Answer Anchoring:** Anchoring the data on the answer generally leads to more stable and often higher "I-Don't-Know Rates," possibly indicating that the model is more aware of its uncertainty when the answer is provided. * **Dataset Sensitivity:** The models exhibit varying levels of uncertainty depending on the dataset. PopQA, in particular, shows a significant reduction in the "I-Don't-Know Rate" for Q-Anchored data after the initial layers, suggesting that the model becomes more confident in its predictions for this dataset as it processes more layers. * **Model Version Comparison:** The Mistral-7B-v0.3 model appears to have improved in terms of reducing uncertainty for the Q-Anchored (PopQA) dataset, as indicated by the lower "I-Don't-Know Rate" after the initial layers. * **Layer-wise Behavior:** The fluctuations in the "I-Don't-Know Rate" across different layers suggest that the model's uncertainty changes as it processes the input through different layers of its neural network architecture. The data suggests that the model's confidence and uncertainty are influenced by the anchoring method (question vs. answer), the specific question-answering dataset, and the depth of the model (layer number). The comparison between the two model versions highlights potential improvements in uncertainty handling in the newer version (v0.3).

## Line Chart: I-Don't-Know Rate vs. Layer for Mistral Models ### Overview The image presents two line charts, side-by-side, comparing the "I-Don't-Know Rate" across different layers of two Mistral language models: Mistral-7B-v0.1 and Mistral-7B-v0.3. The x-axis represents the "Layer" (ranging from 0 to 30), and the y-axis represents the "I-Don't-Know Rate" (ranging from 0 to 100). Each chart displays multiple lines, each representing a different data series based on the anchoring method (Q-Anchored or A-Anchored) and the dataset used (PopQA, TriviaQA, HotpotQA, NQ). ### Components/Axes * **X-axis:** Layer (0 to 30) * **Y-axis:** I-Don't-Know Rate (0 to 100) * **Left Chart Title:** Mistral-7B-v0.1 * **Right Chart Title:** Mistral-7B-v0.3 * **Legend (Bottom Center):** * Blue Line: Q-Anchored (PopQA) * Orange Line: A-Anchored (PopQA) * Green Line: Q-Anchored (TriviaQA) * Light Blue Line: A-Anchored (TriviaQA) * Purple Line: Q-Anchored (HotpotQA) * Red Line: A-Anchored (HotpotQA) * Teal Line: Q-Anchored (NQ) * Gray Line: A-Anchored (NQ) ### Detailed Analysis or Content Details **Mistral-7B-v0.1 (Left Chart):** * **Q-Anchored (PopQA) - Blue Line:** Starts at approximately 95, rapidly decreases to around 15 by layer 10, then fluctuates between 10 and 25 until layer 30. * **A-Anchored (PopQA) - Orange Line:** Starts at approximately 90, decreases to around 50 by layer 10, then gradually decreases to around 30-40, with some fluctuations, until layer 30. * **Q-Anchored (TriviaQA) - Green Line:** Starts at approximately 90, decreases to around 20 by layer 10, then fluctuates between 20 and 30 until layer 30. * **A-Anchored (TriviaQA) - Light Blue Line:** Starts at approximately 95, decreases to around 60 by layer 10, then gradually decreases to around 40-50, with some fluctuations, until layer 30. * **Q-Anchored (HotpotQA) - Purple Line:** Starts at approximately 85, decreases to around 40 by layer 10, then fluctuates between 30 and 50 until layer 30. * **A-Anchored (HotpotQA) - Red Line:** Starts at approximately 90, decreases to around 60 by layer 10, then gradually decreases to around 50-60, with some fluctuations, until layer 30. * **Q-Anchored (NQ) - Teal Line:** Starts at approximately 80, decreases to around 10 by layer 10, then fluctuates between 10 and 20 until layer 30. * **A-Anchored (NQ) - Gray Line:** Starts at approximately 85, decreases to around 40 by layer 10, then gradually decreases to around 30-40, with some fluctuations, until layer 30. **Mistral-7B-v0.3 (Right Chart):** * **Q-Anchored (PopQA) - Blue Line:** Starts at approximately 95, rapidly decreases to around 10 by layer 10, then fluctuates between 10 and 20 until layer 30. * **A-Anchored (PopQA) - Orange Line:** Starts at approximately 90, decreases to around 50 by layer 10, then gradually decreases to around 40-50, with some fluctuations, until layer 30. * **Q-Anchored (TriviaQA) - Green Line:** Starts at approximately 90, decreases to around 20 by layer 10, then fluctuates between 20 and 30 until layer 30. * **A-Anchored (TriviaQA) - Light Blue Line:** Starts at approximately 95, decreases to around 60 by layer 10, then gradually decreases to around 40-50, with some fluctuations, until layer 30. * **Q-Anchored (HotpotQA) - Purple Line:** Starts at approximately 85, decreases to around 40 by layer 10, then fluctuates between 30 and 50 until layer 30. * **A-Anchored (HotpotQA) - Red Line:** Starts at approximately 90, decreases to around 60 by layer 10, then gradually decreases to around 50-60, with some fluctuations, until layer 30. * **Q-Anchored (NQ) - Teal Line:** Starts at approximately 80, decreases to around 10 by layer 10, then fluctuates between 10 and 20 until layer 30. * **A-Anchored (NQ) - Gray Line:** Starts at approximately 85, decreases to around 40 by layer 10, then gradually decreases to around 30-40, with some fluctuations, until layer 30. ### Key Observations * Both models (v0.1 and v0.3) exhibit a significant decrease in "I-Don't-Know Rate" in the initial layers (0-10). * Q-Anchored data series generally have lower "I-Don't-Know Rates" than A-Anchored series, especially for PopQA and NQ datasets. * The "I-Don't-Know Rate" tends to stabilize after layer 10 for most data series. * Mistral-7B-v0.3 consistently shows lower "I-Don't-Know Rates" compared to Mistral-7B-v0.1 across all datasets and anchoring methods. ### Interpretation The charts demonstrate the impact of model depth (layers) and anchoring method on the model's confidence in providing answers. The initial steep decline in "I-Don't-Know Rate" suggests that the early layers of the model are crucial for learning basic knowledge and reducing uncertainty. The difference between Q-Anchored and A-Anchored series indicates that the method used to provide context or guidance to the model influences its confidence. Q-Anchored, which likely involves question-based prompting, appears to be more effective in eliciting responses. The consistent improvement in Mistral-7B-v0.3 over v0.1 suggests that the model updates have resulted in a more knowledgeable and confident model, capable of answering a wider range of questions with greater certainty. The stabilization of the "I-Don't-Know Rate" after layer 10 implies that further increasing model depth may yield diminishing returns in terms of reducing uncertainty. The datasets used (PopQA, TriviaQA, HotpotQA, NQ) represent different types of knowledge and reasoning challenges, and the variations in "I-Don't-Know Rate" across these datasets highlight the model's strengths and weaknesses in different areas.

\n ## Line Charts: I-Don't-Know Rate vs. Layer for Mistral-7B Models ### Overview The image displays two side-by-side line charts comparing the "I-Don't-Know Rate" across the 32 layers (0-31) of two versions of the Mistral-7B language model: v0.1 (left) and v0.3 (right). Each chart plots eight data series, representing two anchoring methods (Q-Anchored and A-Anchored) applied to four different question-answering datasets (PopQA, TriviaQA, HotpotQA, NQ). The charts visualize how the model's propensity to output "I don't know" changes through its layers for different evaluation setups. ### 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. The data appears to cover layers 0 through 31. * **Y-Axis (Both Charts):** * Label: `I-Don't-Know Rate` * Scale: Linear, from 0 to 100, with major ticks at 0, 20, 40, 60, 80, 100. * **Legend (Bottom, spanning both charts):** * The legend is positioned below the two chart panels. * It defines eight series using a combination of color and line style (solid vs. dashed). * **Q-Anchored Series (Solid Lines):** * Blue solid: `Q-Anchored (PopQA)` * Green solid: `Q-Anchored (TriviaQA)` * Purple solid: `Q-Anchored (HotpotQA)` * Pink solid: `Q-Anchored (NQ)` * **A-Anchored Series (Dashed Lines):** * Orange dashed: `A-Anchored (PopQA)` * Red dashed: `A-Anchored (TriviaQA)` * Brown dashed: `A-Anchored (HotpotQA)` * Gray dashed: `A-Anchored (NQ)` ### Detailed Analysis **Chart 1: Mistral-7B-v0.1 (Left Panel)** * **Q-Anchored (Solid Lines) Trend:** All four solid lines exhibit a similar, dramatic pattern. They start at a very high I-Don't-Know Rate (near 100% for PopQA/HotpotQA, ~80% for TriviaQA/NQ) in the earliest layers (0-2). They then plummet sharply within the first 5-7 layers to rates between 10% and 40%. After this initial drop, they fluctuate significantly in the middle and later layers (10-31), with no single clear trend, oscillating roughly between 5% and 50%. * **A-Anchored (Dashed Lines) Trend:** The dashed lines show more varied and generally higher rates than their Q-Anchored counterparts after the initial layers. * `A-Anchored (PopQA)` (Orange dashed): Starts around 40%, rises to ~60% by layer 10, and remains relatively stable between 55-65% for the rest of the layers. * `A-Anchored (TriviaQA)` (Red dashed): Starts high (~80%), dips slightly, then climbs to the highest sustained rate on the chart, fluctuating between 70-90% from layer 10 onward. * `A-Anchored (HotpotQA)` (Brown dashed): Starts around 60%, shows a gradual upward trend, ending near 70-75%. * `A-Anchored (NQ)` (Gray dashed): Starts around 50%, rises to ~70% by layer 10, and stays in the 65-75% range. **Chart 2: Mistral-7B-v0.3 (Right Panel)** * **Q-Anchored (Solid Lines) Trend:** The pattern is notably different from v0.1. The initial drop is less severe for some datasets. * `Q-Anchored (PopQA)` (Blue solid): Still shows a sharp drop from ~100% to ~20% within the first 10 layers, then stabilizes at a low rate (10-25%). * `Q-Anchored (TriviaQA)` (Green solid): Drops from ~80% to ~40% by layer 10 and remains in the 30-45% band. * `Q-Anchored (HotpotQA)` (Purple solid): Drops from ~90% to ~50% by layer 10, then fluctuates between 40-60%. * `Q-Anchored (NQ)` (Pink solid): Drops from ~70% to ~40% by layer 10, then fluctuates between 30-50%. * **A-Anchored (Dashed Lines) Trend:** These lines are more tightly clustered and stable compared to v0.1. * All four dashed lines (Orange, Red, Brown, Gray) converge into a band between approximately 60% and 85% after layer 10. * `A-Anchored (TriviaQA)` (Red dashed) and `A-Anchored (PopQA)` (Orange dashed) are generally at the top of this band (70-85%). * `A-Anchored (HotpotQA)` (Brown dashed) and `A-Anchored (NQ)` (Gray dashed) are slightly lower (60-75%). ### Key Observations 1. **Anchoring Method Effect:** Across both model versions, the **A-Anchored** evaluation (dashed lines) consistently results in a higher I-Don't-Know Rate in the middle and later layers compared to the **Q-Anchored** evaluation (solid lines) for the same dataset. 2. **Model Version Difference:** The transition from v0.1 to v0.3 shows a clear change in behavior. In v0.3, the Q-Anchored rates stabilize at higher levels for most datasets (except PopQA), and the A-Anchored rates become more uniform and clustered. 3. **Dataset Sensitivity:** The PopQA dataset (blue/orange) often shows the most extreme behavior—the highest starting point and the lowest stabilized point for Q-Anchored, and a strong rise for A-Anchored. TriviaQA (green/red) tends to have the highest sustained A-Anchored rates. 4. **Layer Sensitivity:** The most significant changes in rate occur in the first 10 layers. Layers beyond 10 show more stable, though still fluctuating, behavior. ### Interpretation These charts investigate how a large language model's internal representations evolve across its layers, specifically regarding its uncertainty or refusal to answer ("I don't know"). The "anchoring" likely refers to what part of the prompt the model's internal state is measured on—the question (Q) or the answer (A). * **The Core Finding:** The stark difference between Q-Anchored and A-Anchored lines suggests that the model's internal "certainty" is highly dependent on the context it is given. When probed based on the question alone (Q-Anchored), the model's uncertainty drops rapidly in early layers, indicating it quickly forms a potential answer pathway. However, when probed based on a provided answer (A-Anchored), uncertainty remains high, suggesting the model maintains a critical or verifying stance towards supplied information. * **Evolution from v0.1 to v0.3:** The changes in v0.3 imply a shift in the model's internal processing. The higher stabilized Q-Anchored rates (for datasets other than PopQA) might indicate the updated model is more conservative or less confident in its internal answer representations. The convergence of A-Anchored rates suggests a more uniform verification mechanism across different knowledge domains in the newer version. * **Practical Implication:** This data is crucial for understanding model reliability and for techniques like activation steering or uncertainty quantification. It shows that a model's "confidence" is not a single value but a dynamic property that varies by layer, evaluation method, and the specific knowledge domain (dataset). The high A-Anchored rates, especially in v0.3, could be leveraged to detect when the model is being fed incorrect information, as its internal state remains highly uncertain.

## Line Chart: I-Don't-Know Rate Across Layers for Mistral-7B Models ### Overview The image contains two side-by-side line charts comparing the "I-Don't-Know Rate" (IDK Rate) across 30 layers for two versions of the Mistral-7B model (v0.1 and v0.3). Each chart includes multiple data series representing different Q-Anchored and A-Anchored models across four datasets: PopQA, TriviaQA, HotpotQA, and NQ. The charts use color-coded lines with solid (Q-Anchored) and dashed (A-Anchored) styles to distinguish between anchoring methods. ### Components/Axes - **X-axis**: "Layer" (0 to 30), representing the depth of the model's layers. - **Y-axis**: "I-Don't-Know Rate" (0 to 100), indicating the percentage of instances where the model responded with "I don't know." - **Legend**: - **Solid lines**: Q-Anchored models (e.g., Q-Anchored (PopQA), Q-Anchored (TriviaQA), etc.). - **Dashed lines**: A-Anchored models (e.g., A-Anchored (PopQA), A-Anchored (TriviaQA), etc.). - **Colors**: - Blue: Q-Anchored (PopQA) - Green: Q-Anchored (TriviaQA) - Orange: Q-Anchored (HotpotQA) - Red: Q-Anchored (NQ) - Purple: A-Anchored (PopQA) - Gray: A-Anchored (TriviaQA) - Dark gray: A-Anchored (HotpotQA) - Light gray: A-Anchored (NQ) ### Detailed Analysis #### Mistral-7B-v0.1 - **Q-Anchored (PopQA)**: Starts at ~90% at layer 0, drops sharply to ~30% by layer 10, then fluctuates between 20-40%. - **A-Anchored (PopQA)**: Starts at ~50%, remains relatively stable (~40-60%) across layers. - **Q-Anchored (TriviaQA)**: Peaks at ~80% at layer 0, drops to ~20% by layer 10, then fluctuates between 10-30%. - **A-Anchored (TriviaQA)**: Starts at ~60%, decreases to ~30% by layer 10, then stabilizes (~20-40%). - **Q-Anchored (HotpotQA)**: Peaks at ~70% at layer 0, drops to ~20% by layer 10, then fluctuates between 10-30%. - **A-Anchored (HotpotQA)**: Starts at ~50%, decreases to ~20% by layer 10, then stabilizes (~10-30%). - **Q-Anchored (NQ)**: Peaks at ~60% at layer 0, drops to ~10% by layer 10, then fluctuates between 5-20%. - **A-Anchored (NQ)**: Starts at ~40%, decreases to ~10% by layer 10, then stabilizes (~5-15%). #### Mistral-7B-v0.3 - **Q-Anchored (PopQA)**: Starts at ~70%, drops to ~20% by layer 10, then fluctuates between 10-30%. - **A-Anchored (PopQA)**: Starts at ~50%, remains stable (~40-60%) across layers. - **Q-Anchored (TriviaQA)**: Peaks at ~60% at layer 0, drops to ~10% by layer 10, then fluctuates between 5-20%. - **A-Anchored (TriviaQA)**: Starts at ~50%, decreases to ~20% by layer 10, then stabilizes (~10-30%). - **Q-Anchored (HotpotQA)**: Peaks at ~60% at layer 0, drops to ~10% by layer 10, then fluctuates between 5-20%. - **A-Anchored (HotpotQA)**: Starts at ~40%, decreases to ~10% by layer 10, then stabilizes (~5-15%). - **Q-Anchored (NQ)**: Peaks at ~50% at layer 0, drops to ~5% by layer 10, then fluctuates between 2-10%. - **A-Anchored (NQ)**: Starts at ~30%, decreases to ~5% by layer 10, then stabilizes (~2-10%). ### Key Observations 1. **Q-Anchored models** (solid lines) exhibit higher variability and sharper declines in IDK rates compared to A-Anchored models (dashed lines). 2. **A-Anchored models** show more stability, with gradual declines or consistent rates across layers. 3. **Dataset-specific trends**: - **PopQA**: Q-Anchored models start with the highest IDK rates (up to 90% in v0.1) but decline sharply. - **TriviaQA**: Q-Anchored models have the most dramatic drops (e.g., 80% to 20% in v0.1). - **NQ**: Q-Anchored models show the steepest declines (e.g., 60% to 10% in v0.1). 4. **Version differences**: Mistral-7B-v0.3 generally has lower baseline IDK rates than v0.1, suggesting improved performance or reduced uncertainty in later layers. ### Interpretation The data suggests that **Q-Anchored models** (which may prioritize question-specific context) are more sensitive to layer depth, leading to higher initial uncertainty that decreases rapidly. In contrast, **A-Anchored models** (which may rely on broader contextual anchoring) maintain more stable IDK rates, indicating robustness to layer-specific variations. The decline in IDK rates across layers for Q-Anchored models could reflect improved confidence as the model processes deeper layers. However, the variability in trends across datasets highlights that the anchoring method interacts differently with the complexity of each task. The lower baseline rates in v0.3 suggest architectural or training improvements in later versions, though the exact mechanisms remain unclear without additional context.