## 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 100. ### Components/Axes **General Chart Elements:** * **Title (Left Chart):** Comparison of Open-source LLMs * **Title (Middle Chart):** Comparison of Closed-source LLMs * **Title (Right Chart):** Instruction-based vs. Reasoning LLMs * **Y-axis Label:** Scores * **Y-axis Scale:** 0, 20, 40, 60, 80, 100 * **X-axis Label:** Datasets * **X-axis Categories:** HotpotQA, GSM8k, GPQA **Left Chart (Open-source LLMs) Legend:** * **Light Green:** LLaMA3.1-8B * **Yellow:** LLaMA3.1-70B * **Lavender:** Qwen2.5-7B * **Salmon:** Qwen2.5-72B **Middle Chart (Closed-source LLMs) Legend:** * **Salmon:** Qwen2.5-72B * **Orange:** Claude3.5 * **Teal:** GPT-3.5 * **Green:** GPT-4o **Right Chart (Instruction-based vs. Reasoning LLMs) Legend:** * **Salmon:** Qwen2.5-72B * **Green:** GPT-4o * **Pink:** QWQ-32B * **Purple:** DeepSeek-V3 ### Detailed Analysis **Left Chart (Open-source LLMs):** * **LLaMA3.1-8B (Light Green):** * HotpotQA: ~82 * GSM8k: ~79 * GPQA: ~34 * **LLaMA3.1-70B (Yellow):** * HotpotQA: ~91 * GSM8k: ~88 * GPQA: ~35 * **Qwen2.5-7B (Lavender):** * HotpotQA: ~72 * GSM8k: ~85 * GPQA: ~34 * **Qwen2.5-72B (Salmon):** * HotpotQA: ~88 * GSM8k: ~86 * GPQA: ~40 **Middle Chart (Closed-source LLMs):** * **Qwen2.5-72B (Salmon):** * HotpotQA: ~87 * GSM8k: ~86 * GPQA: ~40 * **Claude3.5 (Orange):** * HotpotQA: ~89 * GSM8k: ~95 * GPQA: ~28 * **GPT-3.5 (Teal):** * HotpotQA: ~87 * GSM8k: ~91 * GPQA: ~49 * **GPT-4o (Green):** * HotpotQA: ~92 * GSM8k: ~97 * GPQA: ~36 **Right Chart (Instruction-based vs. Reasoning LLMs):** * **Qwen2.5-72B (Salmon):** * HotpotQA: ~88 * GSM8k: ~96 * GPQA: ~30 * **GPT-4o (Green):** * HotpotQA: ~92 * GSM8k: ~98 * GPQA: ~32 * **QWQ-32B (Pink):** * HotpotQA: ~89 * GSM8k: ~93 * GPQA: ~28 * **DeepSeek-V3 (Purple):** * HotpotQA: ~89 * GSM8k: ~95 * GPQA: ~31 ### Key Observations * Across all charts, performance on GPQA is significantly lower than on HotpotQA and GSM8k. * In the Open-source LLMs chart, LLaMA3.1-70B (Yellow) generally performs better than LLaMA3.1-8B (Light Green). * In the Closed-source LLMs chart, GPT-4o (Green) and Claude3.5 (Orange) show high performance on HotpotQA and GSM8k. * In the Instruction-based vs. Reasoning LLMs chart, GPT-4o (Green) consistently scores high on all datasets. ### Interpretation The charts provide a comparative analysis of LLM performance across different model types and datasets. The lower scores on GPQA suggest that all models struggle with this particular dataset, possibly indicating a higher level of complexity or a different type of reasoning required. The Open-source LLM comparison shows the impact of model size (70B vs. 8B parameters) on performance. The Closed-source and Instruction-based/Reasoning charts highlight the strengths of models like GPT-4o and Claude3.5 in specific tasks. The data suggests that model architecture and training data play a significant role in determining LLM performance on different benchmarks.

\n ## Bar Charts: LLM Performance Comparison ### Overview The image presents three side-by-side bar charts comparing the performance of various Large Language Models (LLMs) across three datasets: HotpotQA, GSM8k, and GPQA. The charts are categorized into Open-source LLMs, Closed-source LLMs, and a comparison of Instruction-based vs. Reasoning LLMs. The y-axis represents "Scores," ranging from 0 to 100. The x-axis represents the datasets. ### Components/Axes * **Y-axis:** "Scores" (0 to 100, linear scale) * **X-axis:** "Datasets" (HotpotQA, GSM8k, GPQA) * **Chart 1 (Open-source LLMs):** * Legend: * LLaMA3-1.8B (Light Blue) * LLaMA3-70B (Pale Green) * Qwen2.5-7B (Light Orange) * Qwen2.5-72B (Light Red) * **Chart 2 (Closed-source LLMs):** * Legend: * Qwen2.5-72B (Light Orange) * Claude3.5 (Pale Yellow) * GPT-3.5 (Light Brown) * GPT-4o (Light Purple) * **Chart 3 (Instruction-based vs. Reasoning LLMs):** * Legend: * Qwen2.5-72B (Light Orange) * GPT-3.5 (Light Brown) * QWO-32B (Light Green) * DeepSeek-V3 (Dark Green) ### Detailed Analysis **Chart 1: Comparison of Open-source LLMs** * **HotpotQA:** LLaMA3-1.8B scores approximately 84. LLaMA3-70B scores approximately 88. Qwen2.5-7B scores approximately 86. Qwen2.5-72B scores approximately 89. * **GSM8k:** LLaMA3-1.8B scores approximately 86. LLaMA3-70B scores approximately 92. Qwen2.5-7B scores approximately 88. Qwen2.5-72B scores approximately 91. * **GPQA:** LLaMA3-1.8B scores approximately 34. LLaMA3-70B scores approximately 38. Qwen2.5-7B scores approximately 36. Qwen2.5-72B scores approximately 42. **Chart 2: Comparison of Closed-source LLMs** * **HotpotQA:** Qwen2.5-72B scores approximately 90. Claude3.5 scores approximately 94. GPT-3.5 scores approximately 88. GPT-4o scores approximately 96. * **GSM8k:** Qwen2.5-72B scores approximately 92. Claude3.5 scores approximately 95. GPT-3.5 scores approximately 90. GPT-4o scores approximately 97. * **GPQA:** Qwen2.5-72B scores approximately 40. Claude3.5 scores approximately 44. GPT-3.5 scores approximately 32. GPT-4o scores approximately 46. **Chart 3: Instruction-based vs. Reasoning LLMs** * **HotpotQA:** Qwen2.5-72B scores approximately 90. GPT-3.5 scores approximately 88. QWO-32B scores approximately 92. DeepSeek-V3 scores approximately 94. * **GSM8k:** Qwen2.5-72B scores approximately 92. GPT-3.5 scores approximately 90. QWO-32B scores approximately 94. DeepSeek-V3 scores approximately 96. * **GPQA:** Qwen2.5-72B scores approximately 38. GPT-3.5 scores approximately 32. QWO-32B scores approximately 36. DeepSeek-V3 scores approximately 40. ### Key Observations * GPT-4o consistently achieves the highest scores across all datasets in the Closed-source LLM comparison. * LLaMA3-70B generally outperforms LLaMA3-1.8B across all datasets. * DeepSeek-V3 consistently achieves the highest scores across all datasets in the Instruction-based vs. Reasoning LLMs comparison. * Performance on GPQA is significantly lower than on HotpotQA and GSM8k for all models. * Qwen2.5-72B performs well across all charts, often being competitive with larger models. ### Interpretation The data suggests that model size and architecture significantly impact performance on these LLM benchmarks. Larger models (e.g., LLaMA3-70B, GPT-4o, DeepSeek-V3) generally achieve higher scores. The consistent high performance of GPT-4o and DeepSeek-V3 indicates their superior capabilities in question answering and reasoning tasks. The lower scores on the GPQA dataset suggest that this dataset presents a greater challenge for the models, potentially due to its specific characteristics or complexity. The comparison between open-source and closed-source models highlights the advancements made by proprietary models, although open-source models are rapidly improving, as demonstrated by the performance of Qwen2.5-72B and LLaMA3-70B. The final chart suggests that models specifically designed for instruction-following and reasoning (like DeepSeek-V3 and QWO-32B) can outperform general-purpose models (like GPT-3.5) on these tasks. The consistent trends across datasets suggest the results are not random and reflect genuine differences in model capabilities.

## Bar Charts: LLM Performance Comparison Across Datasets ### Overview The image displays three side-by-side bar charts comparing the performance scores of various Large Language Models (LLMs) on three distinct evaluation datasets: HotpotQA, GSM8k, and GPQA. The charts are categorized by model type: open-source, closed-source, and a comparison of instruction-tuned versus reasoning-focused models. ### Components/Axes * **Chart Titles (Top):** * Left: "Comparison of Open-source LLMs" * Center: "Comparison of Closed-source LLMs" * Right: "Instruction-based vs Reasoning LLMs" * **Y-Axis (All Charts):** Labeled "Scores". The scale runs from 0 to 100 with major tick marks at 0, 20, 40, 60, 80, and 100. * **X-Axis (All Charts):** Labeled "Datasets". The three categorical datasets are, from left to right: "HotpotQA", "GSM8k", and "GPQA". * **Legends (Top-Right of each chart):** * **Left Chart (Open-source):** * Teal bar: `LLaMA3.1-8B` * Yellow bar: `LLaMA3.1-70B` * Light purple bar: `Qwen2.5-7B` * Salmon/Red bar: `Qwen2.5-72B` * **Center Chart (Closed-source):** * Salmon/Red bar: `Qwen2.5-72B` * Blue bar: `Claude3.5` * Yellow bar: `GPT-3.5` * Green bar: `GPT-4o` * **Right Chart (Instruction vs Reasoning):** * Salmon/Red bar: `Qwen2.5-72B` * Green bar: `GPT-4o` * Pink bar: `QWQ-32B` * Purple bar: `DeepSeek-V3` ### Detailed Analysis **1. Left Chart: Comparison of Open-source LLMs** * **HotpotQA:** `LLaMA3.1-70B` (yellow) scores highest (~90), followed by `Qwen2.5-72B` (salmon, ~88), `LLaMA3.1-8B` (teal, ~82), and `Qwen2.5-7B` (purple, ~72). * **GSM8k:** `LLaMA3.1-70B` (yellow) again leads (~90), with `Qwen2.5-72B` (salmon, ~86) and `LLaMA3.1-8B` (teal, ~80) close behind. `Qwen2.5-7B` (purple) scores ~78. * **GPQA:** All models show a dramatic performance drop. `Qwen2.5-72B` (salmon) scores highest (~40), followed by `LLaMA3.1-70B` (yellow, ~38), `Qwen2.5-7B` (purple, ~36), and `LLaMA3.1-8B` (teal, ~35). * **Trend:** Performance is relatively high and stable for HotpotQA and GSM8k but collapses for GPQA across all open-source models. Larger models (70B/72B) consistently outperform their smaller counterparts (8B/7B). **2. Center Chart: Comparison of Closed-source LLMs** * **HotpotQA:** `GPT-4o` (green) scores highest (~92), followed by `Claude3.5` (blue, ~88), `Qwen2.5-72B` (salmon, ~88), and `GPT-3.5` (yellow, ~86). * **GSM8k:** `GPT-4o` (green) leads (~94), with `Claude3.5` (blue, ~92) and `Qwen2.5-72B` (salmon, ~90) close. `GPT-3.5` (yellow) scores ~88. * **GPQA:** A significant drop occurs again. `Claude3.5` (blue) scores highest (~50), followed by `GPT-4o` (green, ~36), `Qwen2.5-72B` (salmon, ~40), and `GPT-3.5` (yellow, ~28). * **Trend:** Similar to open-source models, performance is strong on HotpotQA/GSM8k but weak on GPQA. `GPT-4o` and `Claude3.5` are the top performers overall. `GPT-3.5` shows the most significant relative decline on GPQA. **3. Right Chart: Instruction-based vs Reasoning LLMs** * **HotpotQA:** `GPT-4o` (green) and `DeepSeek-V3` (purple) are nearly tied for highest (~92), with `Qwen2.5-72B` (salmon, ~88) and `QWQ-32B` (pink, ~86) slightly behind. * **GSM8k:** `GPT-4o` (green) leads (~94), followed by `DeepSeek-V3` (purple, ~92), `Qwen2.5-72B` (salmon, ~90), and `QWQ-32B` (pink, ~88). * **GPQA:** `DeepSeek-V3` (purple) scores highest (~40), followed by `Qwen2.5-72B` (salmon, ~40), `GPT-4o` (green, ~36), and `QWQ-32B` (pink, ~32). * **Trend:** The pattern of high performance on the first two datasets and low performance on GPQA persists. `GPT-4o` and `DeepSeek-V3` are the strongest models in this comparison. The reasoning-focused model `QWQ-32B` (pink) generally scores lower than the others, especially on GPQA. ### Key Observations 1. **Dataset Difficulty:** GPQA is universally the most challenging dataset, causing a performance drop of 40-60 points for every model compared to HotpotQA and GSM8k. 2. **Model Scaling:** Within model families (e.g., LLaMA, Qwen), larger parameter models (70B/72B) consistently outperform smaller ones (8B/7B). 3. **Top Performers:** `GPT-4o` and `Claude3.5` are the top-performing closed-source models. Among open-source models, `Qwen2.5-72B` and `LLaMA3.1-70B` are the strongest. 4. **Instruction vs. Reasoning:** The dedicated reasoning model `QWQ-32B` does not outperform general instruction-tuned models like `GPT-4o` or `DeepSeek-V3` on these benchmarks, and it scores the lowest on the difficult GPQA task. ### Interpretation The data suggests a clear hierarchy of task difficulty for current LLMs. HotpotQA (likely a multi-hop reasoning QA task) and GSM8k (grade-school math) appear to be tasks where state-of-the-art models have achieved high proficiency. In contrast, GPQA (likely a more complex, specialized, or adversarial dataset) represents a significant frontier where all models, regardless of size or training paradigm (open/closed, instruction/reasoning), struggle. The consistent performance gap between model sizes underscores the continued importance of scale. The strong showing of `DeepSeek-V3` and `Qwen2.5-72B` indicates that the performance gap between leading open-source and closed-source models is narrow on these specific benchmarks. However, the catastrophic drop on GPQA for all models implies that current evaluation metrics may not fully capture robustness or generalization to highly complex problems. The charts collectively highlight that while LLMs have mastered certain benchmark tasks, achieving reliable performance on more demanding, real-world-like challenges remains an open problem.

## Bar Chart: Comparison of LLMs Across Datasets ### Overview The image contains three grouped bar charts comparing the performance of various large language models (LLMs) across three datasets: **HotpotQA**, **GSM8k**, and **GPQA**. Each chart focuses on a different category of LLMs: 1. **Open-source LLMs** 2. **Closed-source LLMs** 3. **Instruction-based vs. Reasoning LLMs** The y-axis represents scores (0–100), and the x-axis lists datasets. Legends on the right map colors to specific models. --- ### Components/Axes #### Labels and Legends - **X-axis (Datasets)**: - HotpotQA - GSM8k - GPQA - **Y-axis (Scores)**: - Scale: 0 to 100 (increments of 20) - **Legends**: - **Open-source LLMs**: - LLaMA 3.1-8B (teal) - LLaMA 3.1-70B (yellow) - Qwen 2.5-72B (red) - GPQA (purple) - **Closed-source LLMs**: - Qwen 2.5-72B (red) - Claude 3.5 (blue) - GPT-3.5 (orange) - GPT-4o (green) - **Instruction-based vs. Reasoning LLMs**: - Qwen 2.5-72B (red) - GPT-4o (green) - QWQ-32B (pink) - DeepSeek-V3 (purple) #### Spatial Grounding - Legends are positioned on the **right** of each chart. - Bars are grouped by dataset, with colors matching the legend labels. --- ### Detailed Analysis #### Open-source LLMs - **HotpotQA**: - LLaMA 3.1-8B: ~80 - LLaMA 3.1-70B: ~90 - Qwen 2.5-72B: ~85 - GPQA: ~35 - **GSM8k**: - LLaMA 3.1-8B: ~80 - LLaMA 3.1-70B: ~85 - Qwen 2.5-72B: ~85 - GPQA: ~35 - **GPQA**: - All models score ~35 (lowest performance). #### Closed-source LLMs - **HotpotQA**: - Qwen 2.5-72B: ~85 - Claude 3.5: ~88 - GPT-3.5: ~82 - GPT-4o: ~90 - **GSM8k**: - Qwen 2.5-72B: ~85 - Claude 3.5: ~90 - GPT-3.5: ~82 - GPT-4o: ~90 - **GPQA**: - Qwen 2.5-72B: ~40 - Claude 3.5: ~50 - GPT-3.5: ~30 - GPT-4o: ~35 #### Instruction-based vs. Reasoning LLMs - **HotpotQA**: - Qwen 2.5-72B: ~85 - GPT-4o: ~90 - QWQ-32B: ~88 - DeepSeek-V3: ~88 - **GSM8k**: - Qwen 2.5-72B: ~85 - GPT-4o: ~90 - QWQ-32B: ~88 - DeepSeek-V3: ~90 - **GPQA**: - Qwen 2.5-72B: ~40 - GPT-4o: ~35 - QWQ-32B: ~30 - DeepSeek-V3: ~40 --- ### Key Observations 1. **Open-source models** (LLaMA, Qwen) perform well on **HotpotQA** and **GSM8k** but struggle with **GPQA** (scores ~35). 2. **Closed-source models** (GPT-4o, Claude 3.5) consistently outperform open-source models, with **GPT-4o** achieving the highest scores (~90) across datasets. 3. **Instruction-based models** (GPT-4o, DeepSeek-V3) dominate **GSM8k** and **HotpotQA**, while **reasoning models** (Qwen, QWQ) lag slightly. 4. **GPQA** is the most challenging dataset, with all models scoring below 50. --- ### Interpretation - **Performance Trends**: - Closed-source models (e.g., GPT-4o) leverage advanced architectures and training data, resulting in higher scores. - Open-source models (e.g., LLaMA) show diminishing returns with larger parameter sizes (8B vs. 70B) on GPQA, suggesting architectural limitations. - Instruction-based models (GPT-4o, DeepSeek-V3) excel in reasoning tasks (GSM8k), while reasoning-focused models (Qwen, QWQ) underperform in GPQA. - **Anomalies**: - GPQA scores are uniformly low, indicating it tests niche or complex reasoning not fully addressed by current LLMs. - QWQ-32B (reasoning model) underperforms in GPQA despite its specialization, suggesting dataset-specific weaknesses. - **Implications**: - Closed-source models remain the benchmark for high-stakes reasoning tasks. - Open-source models require architectural improvements (e.g., better parameter efficiency) to compete with closed-source counterparts. - GPQA highlights a gap in evaluating multi-step reasoning, as no model achieves >50. This analysis underscores the trade-offs between open-source accessibility and closed-source performance, with GPQA serving as a critical benchmark for future LLM development.