## Bar Chart: Llama-3.2-1B vs. Llama-3.2-3B Performance on Question Answering Datasets ### Overview The image presents two bar charts comparing the performance of Llama-3.2-1B and Llama-3.2-3B models on four question answering datasets: PopQA, TriviaQA, HotpotQA, and NQ. The charts display the difference in performance (-ΔP) between two anchoring methods (Q-Anchored and A-Anchored) for each dataset. ### Components/Axes * **Titles:** * Left Chart: Llama-3.2-1B * Right Chart: Llama-3.2-3B * **Y-axis:** * Label: -ΔP * Scale: 0 to 60, with tick marks at 0, 20, 40, and 60. * **X-axis:** * Label: Dataset * Categories: PopQA, TriviaQA, HotpotQA, NQ * **Legend:** Located at the bottom of the image. * Q-Anchored: Represented by a light brown/reddish bar. * A-Anchored: Represented by a gray bar. ### Detailed Analysis **Left Chart: Llama-3.2-1B** * **PopQA:** * Q-Anchored: Approximately 45 * A-Anchored: Approximately 2 * **TriviaQA:** * Q-Anchored: Approximately 58 * A-Anchored: Approximately 17 * **HotpotQA:** * Q-Anchored: Approximately 63 * A-Anchored: Approximately 18 * **NQ:** * Q-Anchored: Approximately 22 * A-Anchored: Approximately 10 **Right Chart: Llama-3.2-3B** * **PopQA:** * Q-Anchored: Approximately 23 * A-Anchored: Approximately 7 * **TriviaQA:** * Q-Anchored: Approximately 64 * A-Anchored: Approximately 10 * **HotpotQA:** * Q-Anchored: Approximately 57 * A-Anchored: Approximately 18 * **NQ:** * Q-Anchored: Approximately 33 * A-Anchored: Approximately 11 ### Key Observations * For both models, the Q-Anchored method generally outperforms the A-Anchored method across all datasets. * The performance difference between Q-Anchored and A-Anchored is most significant for TriviaQA and HotpotQA in Llama-3.2-1B. * Llama-3.2-3B shows a more balanced performance across the datasets compared to Llama-3.2-1B. * The A-Anchored performance is consistently low across all datasets and both models. ### Interpretation The charts indicate that the Q-Anchored method is generally more effective than the A-Anchored method for both Llama-3.2-1B and Llama-3.2-3B models. The larger performance differences observed in TriviaQA and HotpotQA for Llama-3.2-1B suggest that the Q-Anchored method may be particularly beneficial for these types of question answering tasks. The more balanced performance of Llama-3.2-3B across datasets could indicate a more robust model that is less sensitive to the specific characteristics of each dataset. The consistently low performance of the A-Anchored method suggests that this approach may have limitations in effectively leveraging the information within these datasets.

## Bar Chart: Performance Comparison of Llama Models on Question Answering Datasets ### Overview The image presents a comparative bar chart illustrating the performance difference (ΔP) between two Llama models – Llama-3.2-1B and Llama-3.2-3B – across four different question answering datasets: PopQA, TriviaQA, HotpotQA, and NQ. The performance metric, ΔP, appears to represent a change in some probability or accuracy score. The chart uses two bars for each dataset, representing "Q-Anchored" and "A-Anchored" approaches. ### Components/Axes * **X-axis:** "Dataset" with categories: PopQA, TriviaQA, HotpotQA, NQ. * **Y-axis:** "−ΔP" (negative Delta P), with a scale ranging from 0 to 60, incrementing by 10. * **Legend:** Located at the bottom-center of the image. * "Q-Anchored" – represented by a light red color (approximately #F08080). * "A-Anchored" – represented by a gray color (approximately #808080). * **Titles:** Two titles are present, one above each chart: "Llama-3.2-1B" and "Llama-3.2-3B". ### Detailed Analysis The chart is divided into two sections, one for each Llama model. **Llama-3.2-1B:** * **PopQA:** Q-Anchored is approximately 44, A-Anchored is approximately 8. * **TriviaQA:** Q-Anchored is approximately 55, A-Anchored is approximately 16. * **HotpotQA:** Q-Anchored is approximately 62, A-Anchored is approximately 18. * **NQ:** Q-Anchored is approximately 28, A-Anchored is approximately 10. **Llama-3.2-3B:** * **PopQA:** Q-Anchored is approximately 22, A-Anchored is approximately 6. * **TriviaQA:** Q-Anchored is approximately 60, A-Anchored is approximately 12. * **HotpotQA:** Q-Anchored is approximately 54, A-Anchored is approximately 16. * **NQ:** Q-Anchored is approximately 30, A-Anchored is approximately 8. **Trends:** * For both models, the Q-Anchored bars are consistently higher than the A-Anchored bars across all datasets, indicating better performance with the Q-Anchored approach. * The Q-Anchored performance is highest on the TriviaQA and HotpotQA datasets for both models. * The A-Anchored performance is relatively low and consistent across all datasets for both models. * The Llama-3.2-3B model generally shows lower Q-Anchored values than the Llama-3.2-1B model for PopQA, but higher values for TriviaQA, HotpotQA, and NQ. ### Key Observations * The difference between Q-Anchored and A-Anchored performance is substantial, suggesting that the anchoring method significantly impacts the model's performance. * The performance varies considerably depending on the dataset. * The Llama-3.2-3B model shows a different performance profile compared to the Llama-3.2-1B model, particularly on the PopQA dataset. ### Interpretation The data suggests that the "Q-Anchored" approach consistently outperforms the "A-Anchored" approach across all tested datasets for both Llama models. This implies that anchoring the model's attention or processing towards the question itself is more effective than anchoring it towards the answer. The varying performance across datasets indicates that the effectiveness of each model is dataset-dependent, potentially due to differences in question complexity, answer format, or domain knowledge required. The Llama-3.2-3B model's performance on PopQA is lower than the Llama-3.2-1B model, which could be due to the larger model being less effective on simpler datasets or requiring more data to generalize effectively. The higher performance of the Llama-3.2-3B model on TriviaQA, HotpotQA, and NQ suggests that it can leverage its increased capacity to handle more complex reasoning and knowledge retrieval tasks. The consistent low performance of the A-Anchored approach suggests that it may not be a suitable strategy for these question answering tasks.

## Bar Charts: Llama-3.2 Model Performance (ΔAP) by Dataset and Anchoring Method ### Overview The image displays two side-by-side vertical bar charts comparing the performance change (ΔAP) of two different-sized language models (Llama-3.2-1B and Llama-3.2-3B) across four question-answering datasets. Performance is measured for two different methods: "Q-Anchored" and "A-Anchored". ### Components/Axes * **Titles:** * Left Chart: `Llama-3.2-1B` * Right Chart: `Llama-3.2-3B` * **Y-Axis (Both Charts):** Labeled `ΔAP`. The scale runs from 0 to 60, with major tick marks at 0, 20, 40, and 60. * **X-Axis (Both Charts):** Labeled `Dataset`. The categories are, from left to right: `PopQA`, `TriviaQA`, `HotpotQA`, `NQ`. * **Legend:** Positioned at the bottom center of the entire image, spanning both charts. * A red/brown square corresponds to `Q-Anchored`. * A gray square corresponds to `A-Anchored`. ### Detailed Analysis **Chart 1: Llama-3.2-1B (Left)** * **Trend Verification:** For all four datasets, the red `Q-Anchored` bar is significantly taller than the gray `A-Anchored` bar. * **Data Points (Approximate ΔAP values):** * **PopQA:** Q-Anchored ≈ 45, A-Anchored ≈ 3 * **TriviaQA:** Q-Anchored ≈ 58, A-Anchored ≈ 18 * **HotpotQA:** Q-Anchored ≈ 65 (exceeds the 60 axis line), A-Anchored ≈ 18 * **NQ:** Q-Anchored ≈ 22, A-Anchored ≈ 10 **Chart 2: Llama-3.2-3B (Right)** * **Trend Verification:** Similar to the 1B model, the `Q-Anchored` bars are consistently taller than the `A-Anchored` bars across all datasets. * **Data Points (Approximate ΔAP values):** * **PopQA:** Q-Anchored ≈ 25, A-Anchored ≈ 8 * **TriviaQA:** Q-Anchored ≈ 65 (exceeds the 60 axis line), A-Anchored ≈ 10 * **HotpotQA:** Q-Anchored ≈ 58, A-Anchored ≈ 18 * **NQ:** Q-Anchored ≈ 35, A-Anchored ≈ 12 ### Key Observations 1. **Dominant Method:** The `Q-Anchored` method yields a substantially higher ΔAP than the `A-Anchored` method for every dataset-model combination shown. 2. **Model Size Impact:** The larger `Llama-3.2-3B` model achieves higher peak ΔAP values (notably on TriviaQA and HotpotQA) compared to the `Llama-3.2-1B` model. 3. **Dataset Sensitivity:** The performance gain (ΔAP) varies significantly by dataset. For the 1B model, HotpotQA shows the highest Q-Anchored gain. For the 3B model, TriviaQA shows the highest gain. 4. **A-Anchored Stability:** The performance of the `A-Anchored` method is relatively low and stable across datasets, generally ranging between ΔAP values of 3 to 18, with less variation than the Q-Anchored method. ### Interpretation The data suggests that for the evaluated Llama-3.2 models, anchoring on the question (`Q-Anchored`) is a far more effective strategy for improving performance (as measured by ΔAP) than anchoring on the answer (`A-Anchored`). This advantage holds across diverse QA datasets (PopQA, TriviaQA, HotpotQA, NQ). The relationship between model size and performance gain is non-uniform. While the 3B model shows a higher maximum gain, the 1B model's largest gain is on HotpotQA, whereas the 3B model's largest gain shifts to TriviaQA. This indicates that the optimal dataset for observing performance improvements may depend on the model's scale. The consistently low ΔAP for the `A-Anchored` method implies that simply conditioning on the answer provides minimal benefit over the baseline, or may even be a less effective prompting/anchoring technique for these tasks. The notable outlier is the Q-Anchored performance on HotpotQA for the 1B model, which is exceptionally high relative to its performance on other datasets, suggesting a particular synergy between that model size, the Q-Anchored method, and the nature of the HotpotQA dataset (which often requires multi-hop reasoning).

## Bar Chart: Performance Comparison of Llama-3.2-1B and Llama-3.2-3B Models ### Overview The image contains two side-by-side bar charts comparing the performance of two language models (Llama-3.2-1B and Llama-3.2-3B) across four question-answering datasets (PopQA, TriviaQA, HotpotQA, NQ). Performance is measured using the metric "-ΔP" (negative delta P), with separate bars for "Q-Anchored" (red) and "A-Anchored" (gray) approaches. The charts highlight differences in performance between model sizes and anchoring strategies. ### Components/Axes - **X-Axis (Datasets)**: PopQA, TriviaQA, HotpotQA, NQ (categorical, left to right). - **Y-Axis (-ΔP)**: Numerical scale from 0 to 60, with increments of 20. - **Legend**: Located at the bottom center, with red representing "Q-Anchored" and gray representing "A-Anchored". - **Model Labels**: - Left chart: "Llama-3.2-1B" (top-left). - Right chart: "Llama-3.2-3B" (top-right). ### Detailed Analysis #### Llama-3.2-1B (Left Chart) - **PopQA**: - Q-Anchored: ~45 - A-Anchored: ~2 - **TriviaQA**: - Q-Anchored: ~58 - A-Anchored: ~16 - **HotpotQA**: - Q-Anchored: ~62 - A-Anchored: ~16 - **NQ**: - Q-Anchored: ~22 - A-Anchored: ~8 #### Llama-3.2-3B (Right Chart) - **PopQA**: - Q-Anchored: ~23 - A-Anchored: ~5 - **TriviaQA**: - Q-Anchored: ~63 - A-Anchored: ~9 - **HotpotQA**: - Q-Anchored: ~57 - A-Anchored: ~18 - **NQ**: - Q-Anchored: ~32 - A-Anchored: ~10 ### Key Observations 1. **Q-Anchored Dominance**: - Q-Anchored consistently outperforms A-Anchored across all datasets and models. - Llama-3.2-1B achieves the highest Q-Anchored performance on HotpotQA (~62), while Llama-3.2-3B excels on TriviaQA (~63). 2. **Model Size Impact**: - Llama-3.2-3B generally outperforms Llama-3.2-1B in Q-Anchored for TriviaQA and NQ but underperforms on PopQA and HotpotQA. - A-Anchored performance improves slightly with larger models (e.g., HotpotQA: 16 → 18). 3. **NQ Dataset Anomaly**: - Llama-3.2-3B’s Q-Anchored performance (~32) is lower than Llama-3.2-1B’s (~22), contradicting the trend of larger models performing better. 4. **A-Anchored Variability**: - A-Anchored values are consistently low but show modest gains with larger models (e.g., TriviaQA: 16 → 9, HotpotQA: 16 → 18). ### Interpretation The data suggests that **Q-Anchored methods are significantly more effective** than A-Anchored approaches for both models, with performance heavily dependent on the dataset. While Llama-3.2-3B generally improves Q-Anchored results, its underperformance on NQ raises questions about scalability or dataset-specific limitations. The modest gains in A-Anchored performance with larger models indicate potential for optimization but highlight a persistent gap compared to Q-Anchored. The NQ dataset’s anomalous results for Llama-3.2-3B warrant further investigation into model behavior on this specific task.