## Bar Charts: LLM Performance Comparison ### Overview The image presents three bar charts comparing the performance of different Large Language Models (LLMs) on three datasets: HotpotQA, GSM8K, and GPQA. The charts are grouped by LLM type: Open-source, Closed-source, and Instruction-based vs. Reasoning. The y-axis represents "Scores," ranging from 0 to 80. The x-axis represents the "Datasets." ### Components/Axes * **Y-axis:** "Scores," ranging from 0 to 80 in increments of 20. * **X-axis:** "Datasets," with categories: HotpotQA, GSM8K, GPQA. * **Chart 1: Comparison of Open-source LLMs** * **Legend (top-right):** * Light Green: LLaMA3.1-8B * Yellow: LLaMA3.1-70B * Light Purple: Qwen2.5-7B * Salmon: Qwen2.5-72B * **Chart 2: Comparison of Closed-source LLMs** * **Legend (top-right):** * Salmon: Qwen2.5-72B * Light Blue: Claude3.5 * Orange: GPT-3.5 * Green: GPT-4o * **Chart 3: Instruction-based vs. Reasoning LLMs** * **Legend (top-right):** * Salmon: Qwen2.5-72B * Light Green: GPT-4o * Pink: QWQ-32B * Purple: DeepSeek-V3 ### Detailed Analysis **Chart 1: Comparison of Open-source LLMs** * **HotpotQA:** * LLaMA3.1-8B (Light Green): ~72 * LLaMA3.1-70B (Yellow): ~69 * Qwen2.5-7B (Light Purple): ~61 * Qwen2.5-72B (Salmon): ~70 * **GSM8K:** * LLaMA3.1-8B (Light Green): ~59 * LLaMA3.1-70B (Yellow): ~64 * Qwen2.5-7B (Light Purple): ~61 * Qwen2.5-72B (Salmon): ~72 * **GPQA:** * LLaMA3.1-8B (Light Green): ~6 * LLaMA3.1-70B (Yellow): ~16 * Qwen2.5-7B (Light Purple): ~10 * Qwen2.5-72B (Salmon): ~12 **Chart 2: Comparison of Closed-source LLMs** * **HotpotQA:** * Qwen2.5-72B (Salmon): ~70 * Claude3.5 (Light Blue): ~82 * GPT-3.5 (Orange): ~72 * GPT-4o (Green): ~73 * **GSM8K:** * Qwen2.5-72B (Salmon): ~72 * Claude3.5 (Light Blue): ~78 * GPT-3.5 (Orange): ~73 * GPT-4o (Green): ~80 * **GPQA:** * Qwen2.5-72B (Salmon): ~11 * Claude3.5 (Light Blue): ~35 * GPT-3.5 (Orange): ~22 * GPT-4o (Green): ~16 **Chart 3: Instruction-based vs. Reasoning LLMs** * **HotpotQA:** * Qwen2.5-72B (Salmon): ~70 * GPT-4o (Light Green): ~72 * QWQ-32B (Pink): ~61 * DeepSeek-V3 (Purple): ~73 * **GSM8K:** * Qwen2.5-72B (Salmon): ~72 * GPT-4o (Light Green): ~80 * QWQ-32B (Pink): ~65 * DeepSeek-V3 (Purple): ~78 * **GPQA:** * Qwen2.5-72B (Salmon): ~11 * GPT-4o (Light Green): ~15 * QWQ-32B (Pink): ~22 * DeepSeek-V3 (Purple): ~27 ### Key Observations * **Open-source LLMs:** Qwen2.5-72B generally performs competitively with LLaMA3.1-70B on HotpotQA and GSM8K, but all open-source models struggle on GPQA. * **Closed-source LLMs:** Claude3.5 and GPT-4o consistently outperform Qwen2.5-72B and GPT-3.5 across all datasets. GPQA remains a challenge, but the scores are significantly higher than for open-source models. * **Instruction-based vs. Reasoning LLMs:** GPT-4o and DeepSeek-V3 show strong performance on GSM8K, suggesting good reasoning capabilities. QWQ-32B generally scores lower than the other models in this category. ### Interpretation The charts provide a comparative analysis of LLM performance across different model architectures (open-source vs. closed-source) and task types (HotpotQA, GSM8K, GPQA). The data suggests that closed-source models like Claude3.5 and GPT-4o generally achieve higher scores, particularly on the more challenging GPQA dataset. This could indicate superior reasoning or knowledge integration capabilities. The open-source models, while competitive on some tasks, appear to struggle with the complexities of GPQA. The Instruction-based vs. Reasoning LLMs chart highlights the varying strengths of different models in this category, with GPT-4o and DeepSeek-V3 showing promise in reasoning tasks. The low scores on GPQA across all model types suggest that this dataset poses a significant challenge for current LLMs.

## Bar Charts: LLM Performance Comparison ### Overview The image presents three bar charts comparing the performance of various Large Language Models (LLMs) across three datasets: HotpotQA, GSM8k, and GPQA. The first chart focuses on open-source LLMs, the second on closed-source LLMs, and the third on instruction-based vs. reasoning LLMs. The y-axis represents "Scores," while the x-axis represents the datasets. ### Components/Axes * **Y-axis:** "Scores" (Scale from 0 to 80, increments of 10) * **X-axis:** "Datasets" (Categories: HotpotQA, GSM8k, GPQA) * **Chart 1 (Open-source LLMs):** * Legend: * LLaMA1-8B (Light Blue) * LLaMA2.1-70B (Yellow) * Owen2.5-7B (Light Orange) * Owen2.5-728 (Red) * **Chart 2 (Closed-source LLMs):** * Legend: * Qwen2.5-72B (Light Green) * Claude3.5 (Purple) * GPT-3.5 (Dark Green) * GPT-4o (Blue) * **Chart 3 (Instruction-based vs. Reasoning LLMs):** * Legend: * Qwen2.5-72B (Light Green) * GPT-4o (Blue) * QWQ-32B (Dark Yellow) * DeepSeek-V3 (Light Purple) ### Detailed Analysis or Content Details **Chart 1: Comparison of Open-source LLMs** * **HotpotQA:** * LLaMA1-8B: Approximately 74 * LLaMA2.1-70B: Approximately 77 * Owen2.5-7B: Approximately 62 * Owen2.5-728: Approximately 65 * **GSM8k:** * LLaMA1-8B: Approximately 60 * LLaMA2.1-70B: Approximately 65 * Owen2.5-7B: Approximately 60 * Owen2.5-728: Approximately 63 * **GPQA:** * LLaMA1-8B: Approximately 10 * LLaMA2.1-70B: Approximately 12 * Owen2.5-7B: Approximately 8 * Owen2.5-728: Approximately 10 **Chart 2: Comparison of Closed-source LLMs** * **HotpotQA:** * Qwen2.5-72B: Approximately 79 * Claude3.5: Approximately 74 * GPT-3.5: Approximately 70 * GPT-4o: Approximately 76 * **GSM8k:** * Qwen2.5-72B: Approximately 72 * Claude3.5: Approximately 68 * GPT-3.5: Approximately 65 * GPT-4o: Approximately 70 * **GPQA:** * Qwen2.5-72B: Approximately 30 * Claude3.5: Approximately 25 * GPT-3.5: Approximately 15 * GPT-4o: Approximately 32 **Chart 3: Instruction-based vs. Reasoning LLMs** * **HotpotQA:** * Qwen2.5-72B: Approximately 74 * GPT-4o: Approximately 76 * QWQ-32B: Approximately 70 * DeepSeek-V3: Approximately 72 * **GSM8k:** * Qwen2.5-72B: Approximately 68 * GPT-4o: Approximately 72 * QWQ-32B: Approximately 65 * DeepSeek-V3: Approximately 67 * **GPQA:** * Qwen2.5-72B: Approximately 10 * GPT-4o: Approximately 12 * QWQ-32B: Approximately 8 * DeepSeek-V3: Approximately 9 ### Key Observations * Across all datasets, LLaMA2.1-70B consistently outperforms LLaMA1-8B. * Qwen2.5-72B generally achieves the highest scores among the closed-source models, particularly on HotpotQA and GSM8k. * GPT-4o consistently performs well across all datasets, often rivaling or exceeding Qwen2.5-72B. * GPQA consistently yields the lowest scores for all models, indicating it is the most challenging dataset. * The performance differences between models are more pronounced on HotpotQA and GSM8k than on GPQA. ### Interpretation The data suggests that larger models (e.g., LLaMA2.1-70B vs. LLaMA1-8B) generally perform better. Closed-source models, particularly Qwen2.5-72B and GPT-4o, tend to outperform open-source models. The varying performance across datasets indicates that the difficulty of the task significantly impacts model performance. GPQA, being the most challenging dataset, reveals the limitations of current LLMs in complex reasoning tasks. The comparison of instruction-based vs. reasoning LLMs (Chart 3) shows that GPT-4o and Qwen2.5-72B are strong performers in both categories, while QWQ-32B and DeepSeek-V3 show slightly lower, but still competitive, results. This suggests that both instruction-following and reasoning capabilities are crucial for achieving high scores on these benchmarks. The consistent trends across the three charts provide a robust basis for comparing the relative strengths and weaknesses of different LLMs.

## [Multi-Panel Bar Chart]: LLM Performance Comparison Across Datasets ### Overview The image displays three horizontally arranged bar charts comparing the performance of various Large Language Models (LLMs) on three benchmark datasets: HotpotQA, GSM8k, and GPQA. The charts are segmented by model type: open-source, closed-source, and instruction-based vs. reasoning models. All charts share a common y-axis labeled "Scores" ranging from 0 to 80, and an x-axis labeled "Datasets". ### Components/Axes * **Overall Layout:** Three distinct bar charts arranged side-by-side. * **Common Y-Axis:** Labeled "Scores", with major tick marks at 0, 20, 40, 60, and 80. * **Common X-Axis:** Labeled "Datasets", with three categorical tick marks: "HotpotQA", "GSM8k", and "GPQA". * **Chart 1 (Left):** Title: "Comparison of Open-source LLMs". Legend (top-right corner): LLaMA3.1-8B (teal), LLaMA3.1-70B (light yellow), Qwen2.5-7B (light purple), Qwen2.5-72B (salmon). * **Chart 2 (Center):** Title: "Comparison of Closed-source LLMs". Legend (top-right corner): Qwen2.5-72B (salmon), Claude3.5 (blue), GPT-3.5 (orange), GPT-4o (green). * **Chart 3 (Right):** Title: "Instruction-based vs. Reasoning LLMs". Legend (top-right corner): Qwen2.5-72B (salmon), GPT-4o (green), QWQ-32B (pink), DeepSeek-V3 (purple). ### Detailed Analysis **Chart 1: Comparison of Open-source LLMs** * **HotpotQA:** LLaMA3.1-8B (~70), LLaMA3.1-70B (~68), Qwen2.5-7B (~60), Qwen2.5-72B (~70). * **GSM8k:** LLaMA3.1-8B (~58), LLaMA3.1-70B (~62), Qwen2.5-7B (~60), Qwen2.5-72B (~72). * **GPQA:** LLaMA3.1-8B (~5), LLaMA3.1-70B (~18), Qwen2.5-7B (~8), Qwen2.5-72B (~12). * **Trend:** Performance is relatively high and clustered on HotpotQA and GSM8k, but drops dramatically for all models on the GPQA dataset. Qwen2.5-72B is the top performer on GSM8k. **Chart 2: Comparison of Closed-source LLMs** * **HotpotQA:** Qwen2.5-72B (~70), Claude3.5 (~80), GPT-3.5 (~72), GPT-4o (~72). * **GSM8k:** Qwen2.5-72B (~72), Claude3.5 (~78), GPT-3.5 (~72), GPT-4o (~80). * **GPQA:** Qwen2.5-72B (~12), Claude3.5 (~42), GPT-3.5 (~22), GPT-4o (~16). * **Trend:** Claude3.5 and GPT-4o show strong, leading performance on HotpotQA and GSM8k. Claude3.5 is a significant outlier on GPQA, achieving a score (~42) more than double that of the next closest model (GPT-3.5 at ~22). **Chart 3: Instruction-based vs. Reasoning LLMs** * **HotpotQA:** Qwen2.5-72B (~70), GPT-4o (~72), QWQ-32B (~55), DeepSeek-V3 (~75). * **GSM8k:** Qwen2.5-72B (~72), GPT-4o (~80), QWQ-32B (~52), DeepSeek-V3 (~72). * **GPQA:** Qwen2.5-72B (~12), GPT-4o (~16), QWQ-32B (~22), DeepSeek-V3 (~30). * **Trend:** GPT-4o leads on GSM8k. DeepSeek-V3 shows the strongest performance on HotpotQA and GPQA among this group. QWQ-32B underperforms on HotpotQA and GSM8k but shows relative strength on GPQA compared to its other scores. ### Key Observations 1. **Dataset Difficulty:** GPQA is universally the most challenging dataset, with all models scoring below 45, and most below 20. 2. **Model Standouts:** Claude3.5 demonstrates exceptional performance on the difficult GPQA dataset. GPT-4o is consistently a top performer across all datasets and model groupings. 3. **Open vs. Closed:** The highest-performing open-source model (Qwen2.5-72B) generally matches or slightly trails the top closed-source models on HotpotQA and GSM8k but falls far behind on GPQA. 4. **Cross-Chart Reference:** Qwen2.5-72B and GPT-4o appear in multiple charts, providing a direct performance bridge between the different model categories. ### Interpretation The data suggests a clear hierarchy in LLM capability based on model architecture and training. Closed-source models, particularly Claude3.5 and GPT-4o, exhibit superior performance, especially on the complex reasoning tasks represented by GPQA. The dramatic performance drop for all models on GPQA indicates this benchmark tests a capability frontier that current models struggle with. The standout performance of Claude3.5 on GPQA suggests it may have a unique architectural or training advantage for that specific type of reasoning. The comparison in the third chart implies that models explicitly designed for reasoning (like DeepSeek-V3) may have an edge on certain tasks (GPQA, HotpotQA) over general instruction-tuned models, though this advantage is not universal across all benchmarks (e.g., GSM8k). The charts collectively highlight that while open-source models are competitive on some tasks, a significant performance gap remains on the most challenging benchmarks.

## Bar Chart: Comparison of LLMs Across Datasets ### Overview The image presents three grouped bar charts comparing the performance of various large language models (LLMs) across three datasets: HotpotQA, GSM8k, and GPQA. The charts are divided into: 1. **Open-source LLMs** (LLaMA, Qwen) 2. **Closed-source LLMs** (Qwen, Claude, GPT) 3. **Instruction-based vs. Reasoning LLMs** (Qwen, GPT, QWQ, DeepSeek) ### Components/Axes - **X-axis**: Datasets (HotpotQA, GSM8k, GPQA) - **Y-axis**: Scores (0–80) - **Legends**: - **Open-source**: LLaMA 3.1-8B (green), LLaMA 3.1-70B (yellow), Qwen 2.5-7B (purple), Qwen 2.5-72B (red) - **Closed-source**: Qwen 2.5-72B (red), Claude 3.5 (blue), GPT-3.5 (orange), GPT-4o (green) - **Instruction vs. Reasoning**: Qwen 2.5-72B (red), GPT-4o (green), QWQ-32B (pink), DeepSeek-V3 (purple) ### Detailed Analysis #### Open-source LLMs - **HotpotQA**: - LLaMA 3.1-8B: ~70 - LLaMA 3.1-70B: ~68 - Qwen 2.5-7B: ~60 - Qwen 2.5-72B: ~70 - **GSM8k**: - LLaMA 3.1-8B: ~60 - LLaMA 3.1-70B: ~65 - Qwen 2.5-7B: ~60 - Qwen 2.5-72B: ~70 - **GPQA**: - LLaMA 3.1-8B: ~5 - LLaMA 3.1-70B: ~15 - Qwen 2.5-7B: ~5 - Qwen 2.5-72B: ~10 #### Closed-source LLMs - **HotpotQA**: - Qwen 2.5-72B: ~70 - Claude 3.5: ~80 - GPT-3.5: ~70 - GPT-4o: ~70 - **GSM8k**: - Qwen 2.5-72B: ~70 - Claude 3.5: ~75 - GPT-3.5: ~70 - GPT-4o: ~80 - **GPQA**: - Qwen 2.5-72B: ~10 - Claude 3.5: ~40 - GPT-3.5: ~20 - GPT-4o: ~15 #### Instruction-based vs. Reasoning LLMs - **HotpotQA**: - Qwen 2.5-72B: ~70 - GPT-4o: ~70 - QWQ-32B: ~50 - DeepSeek-V3: ~70 - **GSM8k**: - Qwen 2.5-72B: ~70 - GPT-4o: ~80 - QWQ-32B: ~50 - DeepSeek-V3: ~70 - **GPQA**: - Qwen 2.5-72B: ~10 - GPT-4o: ~15 - QWQ-32B: ~20 - DeepSeek-V3: ~30 ### Key Observations 1. **Open-source models** (LLaMA, Qwen) show strong performance on HotpotQA and GSM8k but struggle significantly on GPQA (scores <20 for all models). 2. **Closed-source models** (Claude 3.5, GPT-4o) consistently outperform open-source models, especially on GPQA (e.g., Claude 3.5 scores ~40 vs. LLaMA 3.1-70B’s ~15). 3. **Instruction-based models** (QWQ-32B) underperform across all datasets compared to reasoning-focused models like DeepSeek-V3, which achieves ~30 on GPQA (vs. QWQ-32B’s ~20). ### Interpretation - **Model Size vs. Performance**: Larger open-source models (e.g., LLaMA 3.1-70B) outperform smaller variants (8B) but still lag behind closed-source models. - **Closed-source Advantage**: Proprietary models (Claude 3.5, GPT-4o) demonstrate superior reasoning capabilities, particularly on GPQA, suggesting optimized architectures or training data. - **Instruction vs. Reasoning**: Models like DeepSeek-V3 (reasoning-focused) outperform instruction-based models (QWQ-32B) on GPQA, highlighting the importance of reasoning capabilities for complex tasks. - **GPQA as a Bottleneck**: All models score poorly on GPQA, indicating it is a highly challenging dataset requiring advanced reasoning skills. ### Spatial Grounding & Trend Verification - **Legend Placement**: - Open-source: Top-left of first chart - Closed-source: Top-right of second chart - Instruction vs. Reasoning: Top-left of third chart - **Color Consistency**: All colors in legends match bar colors across charts (e.g., red = Qwen 2.5-72B in all contexts). - **Trend Validation**: - Open-source models show a downward trend on GPQA (e.g., LLaMA 3.1-70B drops from ~68 to ~15). - Closed-source models maintain higher scores across datasets (e.g., GPT-4o scores ~70–80). ### Critical Insights - **Open-source Limitations**: While competitive on general tasks (HotpotQA/GSM8k), open-source models lack the reasoning depth for specialized benchmarks like GPQA. - **Closed-source Dominance**: Proprietary models achieve near-human-like performance on reasoning tasks, underscoring the gap between open and closed ecosystems. - **Instruction vs. Reasoning Tradeoff**: Instruction-based models excel at following directions but struggle with abstract reasoning, whereas models like DeepSeek-V3 balance both.