## Bar Chart: Model Accuracy Comparison ### Overview The image is a bar chart comparing the accuracy of different language models under various evaluation conditions. The x-axis represents the models, and the y-axis represents the accuracy score, ranging from 0.0 to 0.8. The chart includes a legend that distinguishes between different accuracy settings (Normal, Extended, Worst) and evaluation prompts (Full-Prompt, Zero-Prompt, Random). ### Components/Axes * **X-axis:** Model names: Qwen 2.5 32B, OLMo 2 32B, Llama 3.2 1B, Gemma 3 1B, Qwen 2.5 1.5B, SmolLM2 1.7B, Granite 3.1 1B, Pythia 1B, PlelAs 1.0 1B, DeepSeek-R1 1.5B * **Y-axis:** Accuracy, ranging from 0.0 to 0.8 in increments of 0.2. * **Legend (Top-Right):** * **Accuracy:** * Normal (khaki color) * Extended (dark olive green color) * Worst (light peach color) * **Evaluation:** * Full-Prompt (gray color) * Zero-Prompt (diagonal lines) * Random (dashed line) ### Detailed Analysis The chart presents accuracy scores for each model under three different accuracy settings (Normal, Extended, Worst) and two evaluation prompts (Full-Prompt, Zero-Prompt). A horizontal dashed line indicates the "Random" baseline. Here's a breakdown of the data for each model: * **Qwen 2.5 32B:** * Normal: ~0.83 * Extended: ~0.66 * Worst: ~0.42 * Full-Prompt: ~0.60 * Zero-Prompt: ~0.25 * **OLMo 2 32B:** * Normal: ~0.84 * Extended: ~0.87 * Worst: ~0.52 * Full-Prompt: ~0.69 * Zero-Prompt: ~0.25 * **Llama 3.2 1B:** * Normal: ~0.63 * Extended: ~0.67 * Worst: ~0.44 * Full-Prompt: ~0.53 * Zero-Prompt: ~0.44 * **Gemma 3 1B:** * Normal: ~0.59 * Extended: ~0.63 * Worst: ~0.44 * Full-Prompt: ~0.51 * Zero-Prompt: ~0.44 * **Qwen 2.5 1.5B:** * Normal: ~0.64 * Extended: ~0.71 * Worst: ~0.44 * Full-Prompt: ~0.53 * Zero-Prompt: ~0.44 * **SmolLM2 1.7B:** * Normal: ~0.70 * Extended: ~0.76 * Worst: ~0.45 * Full-Prompt: ~0.58 * Zero-Prompt: ~0.45 * **Granite 3.1 1B:** * Normal: ~0.67 * Extended: ~0.68 * Worst: ~0.44 * Full-Prompt: ~0.54 * Zero-Prompt: ~0.44 * **Pythia 1B:** * Normal: ~0.52 * Extended: ~0.55 * Worst: ~0.35 * Full-Prompt: ~0.43 * Zero-Prompt: ~0.35 * **PlelAs 1.0 1B:** * Normal: ~0.42 * Extended: ~0.42 * Worst: ~0.32 * Full-Prompt: ~0.41 * Zero-Prompt: ~0.32 * **DeepSeek-R1 1.5B:** * Normal: ~0.49 * Extended: ~0.40 * Worst: ~0.26 * Full-Prompt: ~0.33 * Zero-Prompt: ~0.26 ### Key Observations * The "Extended" accuracy setting generally yields the highest accuracy scores for most models. * The "Worst" accuracy setting consistently results in the lowest accuracy scores. * Qwen 2.5 32B and OLMo 2 32B models show the highest overall accuracy. * PlelAs 1.0 1B and DeepSeek-R1 1.5B models show the lowest overall accuracy. * The "Random" baseline is approximately at 0.25 accuracy. ### Interpretation The chart provides a comparative analysis of the performance of different language models under varying conditions. The "Normal" and "Extended" accuracy settings likely represent different training or fine-tuning strategies, with "Extended" generally leading to better performance. The "Worst" setting likely represents a scenario where the model is intentionally challenged or exposed to adverse conditions. The "Full-Prompt" and "Zero-Prompt" evaluations likely refer to different prompting strategies used during evaluation. "Full-Prompt" may involve providing the model with more context or guidance, while "Zero-Prompt" may involve evaluating the model's ability to generate responses without any specific prompts. The "Random" baseline serves as a reference point, indicating the expected accuracy if the model were simply generating random outputs. Models with accuracy scores significantly above the "Random" baseline demonstrate a degree of learning and generalization. The differences in performance between models highlight the impact of model architecture, training data, and evaluation strategies on overall accuracy.

\n ## Bar Chart: Model Accuracy Evaluation ### Overview The image presents a bar chart comparing the accuracy of several language models across different evaluation methods. The chart displays accuracy scores for "Normal", "Extended", and "Worst" cases, evaluated using "Full-Prompt", "Zero-Prompt", and "Random" prompting strategies. ### Components/Axes * **X-axis:** Model - with the following categories: Qwen 2.5 32B, OLMo 232B, Llama 32 1B, Gemma 3 1B, Qwen 2.5 1.5B, SmollM2 1.7B, Granite 3.1 1B, Pythia 1B, PleiAs 1.0 1B, DeepSeek R1 1.5B. * **Y-axis:** Accuracy - ranging from 0.0 to 0.85, with increments of 0.1. * **Legend:** * **Accuracy:** * Normal (Green) * Extended (Dark Green) * Worst (Light Green) * **Evaluation:** * Full-Prompt (Solid Line) * Zero-Prompt (Hatched Line) * Random (Dashed Line) ### Detailed Analysis The chart consists of grouped bars for each model, representing the accuracy scores under different evaluation conditions. Each model has three bars representing "Normal", "Extended", and "Worst" accuracy, and each bar is further subdivided into three sections representing "Full-Prompt", "Zero-Prompt", and "Random" evaluation. Here's a breakdown of the approximate accuracy values for each model and evaluation method, based on visual estimation: * **Qwen 2.5 32B:** * Normal: Full-Prompt ~0.84, Zero-Prompt ~0.82, Random ~0.80 * Extended: Full-Prompt ~0.75, Zero-Prompt ~0.72, Random ~0.68 * Worst: Full-Prompt ~0.55, Zero-Prompt ~0.50, Random ~0.45 * **OLMo 232B:** * Normal: Full-Prompt ~0.85, Zero-Prompt ~0.83, Random ~0.81 * Extended: Full-Prompt ~0.72, Zero-Prompt ~0.68, Random ~0.64 * Worst: Full-Prompt ~0.50, Zero-Prompt ~0.45, Random ~0.40 * **Llama 32 1B:** * Normal: Full-Prompt ~0.70, Zero-Prompt ~0.68, Random ~0.65 * Extended: Full-Prompt ~0.55, Zero-Prompt ~0.50, Random ~0.45 * Worst: Full-Prompt ~0.35, Zero-Prompt ~0.30, Random ~0.25 * **Gemma 3 1B:** * Normal: Full-Prompt ~0.65, Zero-Prompt ~0.63, Random ~0.60 * Extended: Full-Prompt ~0.50, Zero-Prompt ~0.45, Random ~0.40 * Worst: Full-Prompt ~0.30, Zero-Prompt ~0.25, Random ~0.20 * **Qwen 2.5 1.5B:** * Normal: Full-Prompt ~0.75, Zero-Prompt ~0.72, Random ~0.68 * Extended: Full-Prompt ~0.60, Zero-Prompt ~0.55, Random ~0.50 * Worst: Full-Prompt ~0.40, Zero-Prompt ~0.35, Random ~0.30 * **SmollM2 1.7B:** * Normal: Full-Prompt ~0.70, Zero-Prompt ~0.68, Random ~0.65 * Extended: Full-Prompt ~0.55, Zero-Prompt ~0.50, Random ~0.45 * Worst: Full-Prompt ~0.35, Zero-Prompt ~0.30, Random ~0.25 * **Granite 3.1 1B:** * Normal: Full-Prompt ~0.60, Zero-Prompt ~0.58, Random ~0.55 * Extended: Full-Prompt ~0.45, Zero-Prompt ~0.40, Random ~0.35 * Worst: Full-Prompt ~0.25, Zero-Prompt ~0.20, Random ~0.15 * **Pythia 1B:** * Normal: Full-Prompt ~0.55, Zero-Prompt ~0.53, Random ~0.50 * Extended: Full-Prompt ~0.40, Zero-Prompt ~0.35, Random ~0.30 * Worst: Full-Prompt ~0.20, Zero-Prompt ~0.15, Random ~0.10 * **PleiAs 1.0 1B:** * Normal: Full-Prompt ~0.50, Zero-Prompt ~0.48, Random ~0.45 * Extended: Full-Prompt ~0.35, Zero-Prompt ~0.30, Random ~0.25 * Worst: Full-Prompt ~0.15, Zero-Prompt ~0.10, Random ~0.05 * **DeepSeek R1 1.5B:** * Normal: Full-Prompt ~0.55, Zero-Prompt ~0.53, Random ~0.50 * Extended: Full-Prompt ~0.40, Zero-Prompt ~0.35, Random ~0.30 * Worst: Full-Prompt ~0.20, Zero-Prompt ~0.15, Random ~0.10 ### Key Observations * Qwen 2.5 32B and OLMo 232B consistently demonstrate the highest accuracy across all evaluation methods. * Accuracy generally decreases as the evaluation shifts from "Normal" to "Extended" to "Worst" scenarios. * "Full-Prompt" consistently yields the highest accuracy compared to "Zero-Prompt" and "Random" prompting. * Smaller models (e.g., Pythia 1B, PleiAs 1.0 1B) exhibit significantly lower accuracy scores. * The difference in accuracy between the evaluation methods ("Full-Prompt", "Zero-Prompt", "Random") is more pronounced for higher-performing models. ### Interpretation The chart illustrates the performance of different language models under varying evaluation conditions. The results suggest that model size and prompting strategy significantly impact accuracy. Larger models like Qwen 2.5 32B and OLMo 232B are more robust and maintain higher accuracy even in challenging "Worst" scenarios and with less informative prompting methods like "Random". The consistent superiority of "Full-Prompt" indicates that providing comprehensive context improves model performance. The substantial drop in accuracy for smaller models highlights the importance of model capacity for complex tasks. The differences between "Normal", "Extended", and "Worst" accuracy scores suggest that the models' ability to generalize and handle ambiguous or adversarial inputs varies considerably. This data could be used to inform model selection and prompting strategy optimization for specific applications.

\n ## Grouped Bar Chart: Model Accuracy Comparison ### Overview The image displays a grouped bar chart comparing the accuracy of ten different language models under various evaluation conditions. The chart evaluates each model's performance across three accuracy tiers (Normal, Extended, Worst) and two primary evaluation methods (Full-Prompt, Zero-Prompt), with a baseline for Random performance. ### Components/Axes * **Chart Type:** Grouped bar chart. * **X-Axis (Horizontal):** Labeled "Model". It lists ten distinct language models. From left to right: 1. Qwen 2.5 32B 2. OLMo 2 32B 3. Llama 3.2 1B 4. Gemma 3 1B 5. Qwen 2.5 1.5B 6. SmolLM2 1.7B 7. Granite 3.1 1B 8. Pythia 1B 9. PleIAs 1.0 1B 10. DeepSeek-R1 1.5B * **Y-Axis (Vertical):** Labeled "Accuracy". The scale runs from 0.0 to 0.8, with major gridlines at intervals of 0.2. * **Legend (Top-Right Corner):** Contains two sections. * **Accuracy (Color Key):** * Normal: Olive green solid bar. * Extended: Dark forest green solid bar. * Worst: Light peach/salmon solid bar. * **Evaluation (Pattern Key):** * Full-Prompt: Solid bar (no pattern). * Zero-Prompt: Bar with diagonal hatching (\\). * Random: A horizontal dashed grey line across the chart at y=0.25. * **Baseline:** A horizontal dashed grey line labeled "Random" in the legend, positioned at an accuracy value of 0.25. ### Detailed Analysis For each model, there are two groups of bars: one for "Full-Prompt" (solid) and one for "Zero-Prompt" (hatched). Within each group, three bars represent the "Normal", "Extended", and "Worst" accuracy tiers. **Trend Verification:** Across all models, the "Extended" accuracy (dark green) is consistently the highest bar in its group, followed by "Normal" (olive), and then "Worst" (peach). The "Full-Prompt" evaluation (solid bars) generally yields higher accuracy than the "Zero-Prompt" evaluation (hatched bars) for the same accuracy tier. **Data Points by Model (Approximate Values):** 1. **Qwen 2.5 32B:** * *Full-Prompt:* Normal ~0.83, Extended ~0.86, Worst ~0.49. * *Zero-Prompt:* Normal ~0.61, Extended ~0.65, Worst ~0.40. 2. **OLMo 2 32B:** * *Full-Prompt:* Normal ~0.87, Extended ~0.89, Worst ~0.50. * *Zero-Prompt:* Normal ~0.66, Extended ~0.70, Worst ~0.42. 3. **Llama 3.2 1B:** * *Full-Prompt:* Normal ~0.62, Extended ~0.67, Worst ~0.45. * *Zero-Prompt:* Normal ~0.47, Extended ~0.52, Worst ~0.36. 4. **Gemma 3 1B:** * *Full-Prompt:* Normal ~0.59, Extended ~0.64, Worst ~0.44. * *Zero-Prompt:* Normal ~0.45, Extended ~0.50, Worst ~0.35. 5. **Qwen 2.5 1.5B:** * *Full-Prompt:* Normal ~0.66, Extended ~0.71, Worst ~0.46. * *Zero-Prompt:* Normal ~0.49, Extended ~0.54, Worst ~0.37. 6. **SmolLM2 1.7B:** * *Full-Prompt:* Normal ~0.70, Extended ~0.74, Worst ~0.46. * *Zero-Prompt:* Normal ~0.52, Extended ~0.57, Worst ~0.38. 7. **Granite 3.1 1B:** * *Full-Prompt:* Normal ~0.63, Extended ~0.67, Worst ~0.45. * *Zero-Prompt:* Normal ~0.48, Extended ~0.53, Worst ~0.37. 8. **Pythia 1B:** * *Full-Prompt:* Normal ~0.45, Extended ~0.51, Worst ~0.42. * *Zero-Prompt:* Normal ~0.37, Extended ~0.43, Worst ~0.35. 9. **PleIAs 1.0 1B:** * *Full-Prompt:* Normal ~0.41, Extended ~0.48, Worst ~0.40. * *Zero-Prompt:* Normal ~0.34, Extended ~0.40, Worst ~0.33. 10. **DeepSeek-R1 1.5B:** * *Full-Prompt:* Normal ~0.43, Extended ~0.49, Worst ~0.39. * *Zero-Prompt:* Normal ~0.32, Extended ~0.38, Worst ~0.31. ### Key Observations 1. **Top Performers:** The two largest models, **OLMo 2 32B** and **Qwen 2.5 32B**, achieve the highest accuracies, with OLMo 2 32B showing a slight edge. Their "Extended/Full-Prompt" accuracy approaches 0.9. 2. **Performance Drop with Zero-Prompt:** All models experience a significant drop in accuracy when moving from "Full-Prompt" to "Zero-Prompt" evaluation. The drop is most pronounced for the higher-performing models. 3. **Model Size Correlation:** There is a general trend where the 32B parameter models outperform the 1B-1.7B parameter models. However, among the smaller models, **SmolLM2 1.7B** and **Qwen 2.5 1.5B** perform notably better than others like Pythia 1B or PleIAs 1.0 1B. 4. **"Worst" Case Performance:** The "Worst" accuracy tier (peach bars) shows less variance between models and evaluation methods compared to the "Normal" and "Extended" tiers. Most "Worst" scores cluster between 0.3 and 0.5. 5. **Baseline Comparison:** All models, even under "Zero-Prompt/Worst" conditions, perform above the "Random" baseline of 0.25, except for DeepSeek-R1 1.5B's Zero-Prompt/Worst score which is very close to it (~0.31). ### Interpretation This chart provides a multifaceted evaluation of language model robustness and capability. The data suggests several key insights: * **Prompt Engineering is Critical:** The substantial gap between "Full-Prompt" and "Zero-Prompt" results across all models underscores the heavy reliance of current LLMs on detailed instructions to achieve high performance. Their ability to infer task requirements without explicit prompting ("Zero-Prompt") is significantly weaker. * **Scale Still Matters:** The clear performance advantage of the 32B models indicates that, for this evaluation benchmark, increased model scale correlates strongly with higher accuracy and robustness. * **Performance Tiers Reveal Robustness:** The consistent hierarchy of `Extended > Normal > Worst` accuracy within each model/evaluation group suggests that the benchmark likely has a gradient of difficulty. Models that maintain higher "Worst" scores may be more robust to adversarial or edge-case scenarios. * **Benchmarking Small Models:** The chart allows for direct comparison of similarly sized models (1B-1.7B). SmolLM2 1.7B emerges as a standout among the smaller models, suggesting its architecture or training data may be more effective for this specific task domain. * **The "Random" Baseline:** The 0.25 random baseline implies the task likely involves a four-choice selection (e.g., multiple-choice question). All models demonstrate learned capability beyond random guessing. In summary, this visualization is not just a simple accuracy leaderboard. It reveals the conditional nature of LLM performance, highlighting the importance of prompt design, the benefits of scale, and providing a nuanced view of model robustness across different evaluation scenarios.

## Bar Chart: Model Accuracy Comparison Across Evaluation Methods ### Overview The chart compares the accuracy of various language models (LMs) under three evaluation methods: Normal, Extended, and Worst. It also includes three evaluation types: Full-Prompt, Zero-Prompt, and Random. The y-axis represents accuracy (0–0.8), while the x-axis lists models like Qwen, OLMo, Llama, Gemma, and others. A dashed line at ~0.3 marks the Random baseline. ### Components/Axes - **X-Axis (Models)**: - Qwen 2.5 32B, OLMo 2.32B, Llama 3.2 1B, Gemma 3 1B, Qwen 2.5 1.5B, SmolLM2 1.7B, Granite 3.1 1B, Pythia 1B, Pleias 1.0 1B, DeepSeek-R1 1.5B. - **Y-Axis (Accuracy)**: - Scale from 0.0 to 0.8 in increments of 0.2. - **Legend (Evaluation Methods)**: - **Normal**: Solid olive-green bars. - **Extended**: Solid dark-green bars. - **Worst**: Solid salmon-pink bars. - **Full-Prompt**: Diagonal-striped olive-green bars. - **Zero-Prompt**: Diagonal-striped dark-green bars. - **Random**: Dashed gray line (~0.3 accuracy). ### Detailed Analysis 1. **Model Performance**: - **Qwen 2.5 32B**: - Normal: ~0.82, Extended: ~0.85, Worst: ~0.48. - **OLMo 2.32B**: - Normal: ~0.85, Extended: ~0.70, Worst: ~0.45. - **Llama 3.2 1B**: - Normal: ~0.60, Extended: ~0.65, Worst: ~0.35. - **Gemma 3 1B**: - Normal: ~0.58, Extended: ~0.52, Worst: ~0.32. - **Qwen 2.5 1.5B**: - Normal: ~0.65, Extended: ~0.55, Worst: ~0.38. - **SmolLM2 1.7B**: - Normal: ~0.68, Extended: ~0.58, Worst: ~0.36. - **Granite 3.1 1B**: - Normal: ~0.70, Extended: ~0.50, Worst: ~0.34. - **Pythia 1B**: - Normal: ~0.42, Extended: ~0.45, Worst: ~0.30. - **Pleias 1.0 1B**: - Normal: ~0.40, Extended: ~0.42, Worst: ~0.32. - **DeepSeek-R1 1.5B**: - Normal: ~0.38, Extended: ~0.40, Worst: ~0.28. 2. **Evaluation Methods**: - **Full-Prompt** (diagonal-striped olive-green): - Consistently higher than Zero-Prompt for most models (e.g., Qwen 2.5 32B: ~0.82 vs. ~0.65). - **Zero-Prompt** (diagonal-striped dark-green): - Outperforms Random but lags behind Full-Prompt (e.g., OLMo 2.32B: ~0.70 vs. ~0.85). - **Random** (dashed gray line): - Baseline at ~0.3; all models exceed this except DeepSeek-R1 1.5B in Worst (~0.28). ### Key Observations - **Trends**: - Extended evaluations generally outperform Normal and Worst for most models (e.g., OLMo 2.32B: Extended ~0.70 vs. Normal ~0.85). - Worst evaluations consistently yield the lowest accuracy across all models. - Full-Prompt evaluations achieve the highest accuracy for larger models (e.g., Qwen 2.5 32B: ~0.82). - **Outliers**: - DeepSeek-R1 1.5B underperforms in all categories, with Worst accuracy (~0.28) below the Random baseline. - Pythia 1B and Pleias 1.0 1B show minimal differences between evaluation methods. ### Interpretation The data demonstrates that **evaluation method significantly impacts model performance**. Full-Prompt evaluations consistently yield higher accuracy than Zero-Prompt and Random, suggesting that prompt design is critical for model effectiveness. Extended evaluations often outperform Normal evaluations, indicating that additional context or constraints improve results. However, the Worst evaluation method universally reduces accuracy, highlighting its inefficacy. Larger models (e.g., Qwen, OLMo) maintain higher performance across evaluations, while smaller models (e.g., Pythia, Pleias) struggle, particularly in Worst scenarios. The Random baseline (~0.3) serves as a benchmark, with most models exceeding it except in extreme cases like DeepSeek-R1 1.5B. This underscores the importance of tailored evaluation strategies for different model sizes and tasks.