## Chart Type: Line Graphs Comparing Model Performance ### Overview The image presents two line graphs side-by-side, comparing the answer accuracy of two language models, Llama-3-8B and Llama-3-70B, across different layers. Each graph plots the answer accuracy (y-axis) against the layer number (x-axis) for both question-anchored (Q-Anchored) and answer-anchored (A-Anchored) approaches on four different question answering datasets: PopQA, TriviaQA, HotpotQA, and NQ. The graphs include shaded regions around each line, representing the uncertainty or variance in the accuracy. ### Components/Axes * **Titles:** * Left Graph: "Llama-3-8B" * Right Graph: "Llama-3-70B" * **X-axis:** * Label: "Layer" * Left Graph: Scale from 0 to 30, with tick marks at approximately 0, 10, 20, and 30. * Right Graph: Scale from 0 to 80, with tick marks at approximately 0, 20, 40, 60, and 80. * **Y-axis:** * Label: "Answer Accuracy" * Scale: 0 to 100, with tick marks at 0, 20, 40, 60, 80, and 100. * **Legend:** Located at the bottom of the image. * **Q-Anchored (PopQA):** Solid blue line * **A-Anchored (PopQA):** Dashed brown line * **Q-Anchored (TriviaQA):** Solid green line * **A-Anchored (TriviaQA):** Dashed brown line * **Q-Anchored (HotpotQA):** Solid purple line * **A-Anchored (HotpotQA):** Dashed brown line * **Q-Anchored (NQ):** Dashed pink line * **A-Anchored (NQ):** Dashed brown line ### Detailed Analysis **Left Graph: Llama-3-8B** * **Q-Anchored (PopQA):** (Solid Blue) Starts at approximately 0 accuracy, rises sharply to around 80 by layer 5, and then plateaus between 80 and 100 for the remaining layers. * **A-Anchored (PopQA):** (Dashed Brown) Starts at approximately 50 accuracy, decreases to around 40 by layer 10, and then fluctuates between 30 and 50 for the remaining layers. * **Q-Anchored (TriviaQA):** (Solid Green) Starts at approximately 20 accuracy, rises sharply to around 80 by layer 5, and then plateaus between 80 and 100 for the remaining layers. * **A-Anchored (TriviaQA):** (Dashed Brown) Starts at approximately 50 accuracy, decreases to around 40 by layer 10, and then fluctuates between 30 and 50 for the remaining layers. * **Q-Anchored (HotpotQA):** (Solid Purple) Starts at approximately 50 accuracy, rises sharply to around 80 by layer 5, and then plateaus between 80 and 100 for the remaining layers. * **A-Anchored (HotpotQA):** (Dashed Brown) Starts at approximately 50 accuracy, decreases to around 40 by layer 10, and then fluctuates between 30 and 50 for the remaining layers. * **Q-Anchored (NQ):** (Dashed Pink) Starts at approximately 50 accuracy, rises sharply to around 80 by layer 5, and then plateaus between 80 and 100 for the remaining layers. * **A-Anchored (NQ):** (Dashed Brown) Starts at approximately 50 accuracy, decreases to around 40 by layer 10, and then fluctuates between 30 and 50 for the remaining layers. **Right Graph: Llama-3-70B** * **Q-Anchored (PopQA):** (Solid Blue) Starts at approximately 0 accuracy, rises sharply to around 80 by layer 10, and then fluctuates between 80 and 100 for the remaining layers. * **A-Anchored (PopQA):** (Dashed Brown) Starts at approximately 50 accuracy, decreases to around 40 by layer 20, and then fluctuates between 20 and 50 for the remaining layers. * **Q-Anchored (TriviaQA):** (Solid Green) Starts at approximately 20 accuracy, rises sharply to around 80 by layer 10, and then fluctuates between 80 and 100 for the remaining layers. * **A-Anchored (TriviaQA):** (Dashed Brown) Starts at approximately 50 accuracy, decreases to around 40 by layer 20, and then fluctuates between 20 and 50 for the remaining layers. * **Q-Anchored (HotpotQA):** (Solid Purple) Starts at approximately 50 accuracy, rises sharply to around 80 by layer 10, and then fluctuates between 80 and 100 for the remaining layers. * **A-Anchored (HotpotQA):** (Dashed Brown) Starts at approximately 50 accuracy, decreases to around 40 by layer 20, and then fluctuates between 20 and 50 for the remaining layers. * **Q-Anchored (NQ):** (Dashed Pink) Starts at approximately 50 accuracy, rises sharply to around 80 by layer 10, and then fluctuates between 80 and 100 for the remaining layers. * **A-Anchored (NQ):** (Dashed Brown) Starts at approximately 50 accuracy, decreases to around 40 by layer 20, and then fluctuates between 20 and 50 for the remaining layers. ### Key Observations * For both models, Q-Anchored approaches (PopQA, TriviaQA, HotpotQA, and NQ) generally achieve higher answer accuracy than A-Anchored approaches. * The Llama-3-70B model, with more layers, shows a more gradual increase in accuracy for Q-Anchored approaches compared to the Llama-3-8B model. * The A-Anchored approaches show a similar trend across both models, starting at around 50 accuracy and then decreasing and fluctuating between 20 and 50. * The shaded regions indicate the variance in accuracy, which appears to be larger in the Llama-3-70B model, especially for the Q-Anchored approaches. ### Interpretation The data suggests that question-anchoring is a more effective strategy for achieving high answer accuracy in these language models compared to answer-anchoring. The larger Llama-3-70B model, while showing a more gradual increase in accuracy, ultimately achieves similar performance to the smaller Llama-3-8B model for Q-Anchored approaches. The consistent performance of A-Anchored approaches across both models suggests that this strategy may be less sensitive to model size. The larger variance in accuracy for the Llama-3-70B model could be due to its increased complexity and potential for overfitting. The similar brown dashed line for all A-Anchored approaches suggests that the dataset has little impact on the A-Anchored accuracy.

\n ## Line Chart: Answer Accuracy vs. Layer for Llama Models ### Overview This image presents two line charts comparing the answer accuracy of different question-answering (QA) methods across layers in two Llama models: Llama-3-8B and Llama-3-70B. The x-axis represents the layer number, and the y-axis represents the answer accuracy, ranging from 0 to 100. Each chart displays multiple lines, each representing a different QA method and anchoring strategy. ### Components/Axes * **X-axis:** Layer (ranging from approximately 0 to 30 for Llama-3-8B and 0 to 80 for Llama-3-70B). * **Y-axis:** Answer Accuracy (ranging from 0 to 100). * **Models:** Llama-3-8B (left chart), Llama-3-70B (right chart). * **QA Methods/Anchoring Strategies (Legend):** * Q-Anchored (PopQA) - Blue line * A-Anchored (PopQA) - Orange line * Q-Anchored (TriviaQA) - Purple line * A-Anchored (TriviaQA) - Brown line * Q-Anchored (HotpotQA) - Light Green dashed line * A-Anchored (HotpotQA) - Yellow dashed line * Q-Anchored (NQ) - Teal line * A-Anchored (NQ) - Gray line ### Detailed Analysis or Content Details **Llama-3-8B (Left Chart):** * **Q-Anchored (PopQA):** Starts at approximately 0% accuracy at layer 0, rapidly increases to around 90-95% accuracy by layer 10, and remains relatively stable around 90-95% for the rest of the layers. * **A-Anchored (PopQA):** Starts at approximately 0% accuracy at layer 0, increases to around 40-50% accuracy by layer 10, and plateaus around 40-50% for the remaining layers. * **Q-Anchored (TriviaQA):** Starts at approximately 0% accuracy at layer 0, increases to around 80-90% accuracy by layer 10, and remains relatively stable around 80-90% for the rest of the layers. * **A-Anchored (TriviaQA):** Starts at approximately 0% accuracy at layer 0, increases to around 50-60% accuracy by layer 10, and plateaus around 50-60% for the remaining layers. * **Q-Anchored (HotpotQA):** Starts at approximately 0% accuracy at layer 0, increases to around 80-90% accuracy by layer 10, and remains relatively stable around 80-90% for the rest of the layers. * **A-Anchored (HotpotQA):** Starts at approximately 0% accuracy at layer 0, increases to around 40-50% accuracy by layer 10, and plateaus around 40-50% for the remaining layers. * **Q-Anchored (NQ):** Starts at approximately 0% accuracy at layer 0, increases to around 80-90% accuracy by layer 10, and remains relatively stable around 80-90% for the rest of the layers. * **A-Anchored (NQ):** Starts at approximately 0% accuracy at layer 0, increases to around 40-50% accuracy by layer 10, and plateaus around 40-50% for the remaining layers. **Llama-3-70B (Right Chart):** * **Q-Anchored (PopQA):** Starts at approximately 0% accuracy at layer 0, rapidly increases to around 90-95% accuracy by layer 10, and remains relatively stable around 90-95% for the rest of the layers. * **A-Anchored (PopQA):** Starts at approximately 0% accuracy at layer 0, increases to around 40-50% accuracy by layer 10, and plateaus around 40-50% for the remaining layers. * **Q-Anchored (TriviaQA):** Starts at approximately 0% accuracy at layer 0, increases to around 80-90% accuracy by layer 10, and remains relatively stable around 80-90% for the rest of the layers. * **A-Anchored (TriviaQA):** Starts at approximately 0% accuracy at layer 0, increases to around 50-60% accuracy by layer 10, and plateaus around 50-60% for the remaining layers. * **Q-Anchored (HotpotQA):** Starts at approximately 0% accuracy at layer 0, increases to around 80-90% accuracy by layer 10, and remains relatively stable around 80-90% for the rest of the layers. * **A-Anchored (HotpotQA):** Starts at approximately 0% accuracy at layer 0, increases to around 40-50% accuracy by layer 10, and plateaus around 40-50% for the remaining layers. * **Q-Anchored (NQ):** Starts at approximately 0% accuracy at layer 0, increases to around 80-90% accuracy by layer 10, and remains relatively stable around 80-90% for the rest of the layers. * **A-Anchored (NQ):** Starts at approximately 0% accuracy at layer 0, increases to around 40-50% accuracy by layer 10, and plateaus around 40-50% for the remaining layers. ### Key Observations * **Q-Anchored methods consistently outperform A-Anchored methods** across all QA datasets and both model sizes. * **Accuracy generally increases rapidly in the initial layers (0-10)** and then plateaus. * **The 70B model shows similar trends to the 8B model**, but the x-axis extends to layer 80, indicating a deeper model. * **PopQA, TriviaQA, HotpotQA, and NQ datasets all exhibit similar accuracy curves** for the Q-Anchored methods. * **A-Anchored methods consistently plateau at a lower accuracy level** (around 40-60%) compared to Q-Anchored methods (around 80-95%). ### Interpretation The data suggests that question-anchored methods are significantly more effective than answer-anchored methods for improving answer accuracy in Llama models. This could be because anchoring on the question provides more relevant context for the model to generate accurate answers. The rapid increase in accuracy in the initial layers indicates that the early layers of the model are crucial for capturing fundamental linguistic and semantic information. The plateauing of accuracy after layer 10 suggests that further layers contribute less to overall performance, or that the model has reached its capacity for learning on these datasets. The similarity in trends across different QA datasets suggests that the observed patterns are not specific to any particular dataset but are rather a general characteristic of the model's behavior. The larger 70B model does not fundamentally change the observed trends, indicating that simply increasing model size does not necessarily lead to significant improvements in accuracy without changes to the anchoring strategy. The consistent performance gap between Q- and A-anchored methods highlights the importance of carefully considering the anchoring strategy when training and deploying large language models for question answering.

## Line Charts: Answer Accuracy Across Layers for Llama-3 Models ### Overview The image displays two side-by-side line charts comparing the "Answer Accuracy" of two Large Language Models (LLMs)—Llama-3-8B and Llama-3-70B—across their internal layers. The performance is measured on four different question-answering (QA) datasets using two distinct prompting or anchoring methods: "Q-Anchored" and "A-Anchored." The charts illustrate how model performance evolves from early to late layers. ### Components/Axes * **Chart Titles:** "Llama-3-8B" (left chart), "Llama-3-70B" (right chart). * **Y-Axis (Both Charts):** Label: "Answer Accuracy". Scale: 0 to 100, with major tick marks at intervals of 20 (0, 20, 40, 60, 80, 100). * **X-Axis (Left Chart - Llama-3-8B):** Label: "Layer". Scale: 0 to 30, with major tick marks at intervals of 10 (0, 10, 20, 30). * **X-Axis (Right Chart - Llama-3-70B):** Label: "Layer". Scale: 0 to 80, with major tick marks at intervals of 20 (0, 20, 40, 60, 80). * **Legend (Bottom, spanning both charts):** Contains eight entries, each defined by a line style and color: 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)`: Dash-dot orange line. 6. `A-Anchored (TriviaQA)`: Dash-dot red line. 7. `A-Anchored (HotpotQA)`: Dash-dot gray line. 8. `A-Anchored (NQ)`: Dash-dot light blue line. ### Detailed Analysis **Llama-3-8B (Left Chart):** * **Q-Anchored Series (Solid/Dashed/Dotted Lines):** All four datasets show a similar trend. Accuracy starts very low (near 0-10%) at layer 0, rises sharply to a peak between layers 10-20 (reaching ~90-95% for PopQA/TriviaQA, ~85-90% for HotpotQA/NQ), and then gradually declines or stabilizes at a slightly lower level (~80-90%) towards layer 30. The `Q-Anchored (PopQA)` (solid blue) and `Q-Anchored (TriviaQA)` (solid green) lines are consistently the top performers. * **A-Anchored Series (Dash-dot Lines):** These series exhibit significantly lower accuracy throughout. They start around 40-50% at layer 0, show a slight dip in the early layers (10-15), and then fluctuate between approximately 30% and 45% for the remainder of the layers. There is no strong upward trend; performance remains relatively flat and noisy. The `A-Anchored (TriviaQA)` (dash-dot red) line appears to be the lowest-performing series overall. **Llama-3-70B (Right Chart):** * **Q-Anchored Series:** The pattern is more volatile but follows a similar arc. Accuracy climbs rapidly from layer 0, reaching high levels (>80%) by layer 10. Performance peaks in the middle layers (approximately 20-50), with `Q-Anchored (PopQA)` and `Q-Anchored (TriviaQA)` frequently hitting near 100% accuracy. After layer 50, there is a noticeable downward trend for all Q-Anchored series, ending between 70-90% at layer 80. The lines show more pronounced dips and recoveries compared to the 8B model. * **A-Anchored Series:** Similar to the 8B model, these series perform poorly. They start around 30-40%, dip to their lowest points (some near 10-20%) between layers 20-40, and then recover slightly to fluctuate between 20-40% for the later layers. The `A-Anchored (TriviaQA)` (dash-dot red) line again shows some of the lowest accuracy values. ### Key Observations 1. **Dominant Performance Gap:** There is a stark and consistent separation between the Q-Anchored and A-Anchored methods across both models and all datasets. Q-Anchored prompting yields dramatically higher answer accuracy. 2. **Layer-Wise Arc:** For the effective Q-Anchored method, performance follows an arc: low in very early layers, peaking in the middle layers, and often declining slightly in the final layers. This suggests the model's "knowledge" or answer formulation is most accessible in its intermediate processing stages. 3. **Model Scale Effect:** The larger Llama-3-70B model achieves higher peak accuracies (near 100% for some datasets) but also exhibits greater volatility and a more pronounced late-layer decline compared to the smaller Llama-3-8B. 4. **Dataset Hierarchy:** For Q-Anchored evaluation, PopQA and TriviaQA consistently yield the highest accuracy, followed by HotpotQA and NQ. This ordering is maintained across both models. 5. **A-Anchored Instability:** The A-Anchored lines are not only lower but also noisier, with significant dips, particularly in the 70B model around layers 20-40. ### Interpretation The data strongly suggests that the **method of anchoring or prompting (Q-Anchored vs. A-Anchored) is a far more critical factor for extracting accurate answers from these Llama-3 models than the model size or the specific QA dataset.** The Q-Anchored method, which likely involves framing the query in a specific way relative to the model's internal representations, successfully activates the model's parametric knowledge stored in its middle layers. The observed arc—low early, peak middle, slight decline late—provides a Peircean insight into the model's information processing. The early layers are likely performing low-level feature extraction, the middle layers integrate this into high-level semantic representations where answers are most readily accessible, and the final layers may be specializing for next-token prediction in a way that slightly obfuscates the direct answer retrieval measured here. The greater volatility and higher peak in the 70B model could indicate that its larger capacity allows for more specialized and powerful internal representations (leading to near-perfect scores) but also makes the retrieval process more sensitive to the specific layer, resulting in less stable performance across the network. The consistently poor performance of A-Anchored methods implies that this approach fails to properly interface with the models' knowledge stores, possibly by providing the wrong type of cue or context.

## Line Graph: Answer Accuracy Across Layers for Llama-3-8B and Llama-3-70B Models ### Overview The image compares answer accuracy across layers (0–30 for Llama-3-8B, 0–80 for Llama-3-70B) for two model sizes. It evaluates four datasets (PopQA, TriviaQA, HotpotQA, NQ) using two anchoring methods: Q-Anchored (question-focused) and A-Anchored (answer-focused). Accuracy is measured as a percentage (0–100%). ### Components/Axes - **X-axis**: Layer (0–30 for Llama-3-8B, 0–80 for Llama-3-70B). - **Y-axis**: Answer Accuracy (%) (0–100). - **Legends**: - **Llama-3-8B**: - Blue: Q-Anchored (PopQA) - Green: Q-Anchored (TriviaQA) - Orange: A-Anchored (PopQA) - Red: A-Anchored (TriviaQA) - **Llama-3-70B**: - Purple: Q-Anchored (HotpotQA) - Pink: Q-Anchored (NQ) - Gray: A-Anchored (HotpotQA) - Brown: A-Anchored (NQ) ### Detailed Analysis #### Llama-3-8B (Left Chart) - **Q-Anchored (PopQA, Blue)**: - Starts at ~80% (layer 0), dips to ~60% (layer 10), peaks at ~90% (layer 20), then stabilizes at ~85% (layer 30). - **Q-Anchored (TriviaQA, Green)**: - Begins at ~70%, rises to ~85% (layer 10), fluctuates between ~75–85% (layers 20–30). - **A-Anchored (PopQA, Orange)**: - Starts at ~40%, drops to ~20% (layer 10), recovers to ~50% (layer 30). - **A-Anchored (TriviaQA, Red)**: - Begins at ~50%, dips to ~30% (layer 10), rises to ~60% (layer 30). #### Llama-3-70B (Right Chart) - **Q-Anchored (HotpotQA, Purple)**: - Starts at ~85%, fluctuates between ~70–90% (layers 0–40), stabilizes at ~80% (layers 60–80). - **Q-Anchored (NQ, Pink)**: - Begins at ~75%, dips to ~60% (layer 20), rises to ~85% (layer 60), then drops to ~70% (layer 80). - **A-Anchored (HotpotQA, Gray)**: - Starts at ~50%, fluctuates between ~40–60% (layers 0–60), stabilizes at ~55% (layers 60–80). - **A-Anchored (NQ, Brown)**: - Begins at ~40%, dips to ~30% (layer 20), rises to ~50% (layer 60), then drops to ~45% (layer 80). ### Key Observations 1. **Model Size Impact**: - Llama-3-70B shows smoother trends and higher baseline accuracy (e.g., Q-Anchored HotpotQA peaks at ~90%) compared to Llama-3-8B. 2. **Anchoring Method**: - Q-Anchored methods generally outperform A-Anchored (e.g., Q-Anchored PopQA in Llama-3-8B reaches ~90% vs. A-Anchored PopQA at ~50%). 3. **Dataset Variability**: - NQ dataset exhibits the most erratic trends (e.g., Q-Anchored NQ in Llama-3-70B drops from ~85% to ~70% across layers). 4. **Layer Sensitivity**: - Both models show accuracy dips in mid-layers (e.g., layer 10–20 for Llama-3-8B), suggesting potential architectural bottlenecks. ### Interpretation The data suggests that larger models (Llama-3-70B) maintain higher and more stable accuracy across layers, particularly with Q-Anchored methods. Q-Anchored approaches consistently outperform A-Anchored, likely due to better alignment with question context. The NQ dataset’s volatility may reflect its complexity or noise. Mid-layer dips in both models hint at architectural trade-offs, where certain layers prioritize efficiency over accuracy. The 70B model’s smoother curves imply better generalization, while the 8B model’s sharper fluctuations suggest sensitivity to layer depth.