## Bar Chart: GSM8K Accuracy Comparison
### Overview
The image is a bar chart comparing the accuracy of different Large Language Models (LLMs) on the GSM8K dataset. The chart compares fine-tuned LLMs against versions enhanced with "+SHEPHERD". The y-axis represents accuracy in percentage, and the x-axis represents different LLM configurations.
### Components/Axes
* **Title:** GSM8K
* **Y-axis:** Accuracy (%)
* Scale: 70 to 95, with tick marks at 70, 75, 80, 85, 90, and 95.
* **X-axis:** LLM configurations
* Categories: LLaMA2-70B MAmmoTH, LLaMA2-70B WizardMATH, LLaMA2-70B MetaMATH, LLemma-34B MetaMATH*, DeepSeek-67B MetaMATH*
* **Legend:** Located at the top of the chart.
* Blue: Fine-tuned LLMs
* Orange: +SHEPHERD
* **Horizontal Lines:**
* GPT-4-0613*: 94.4 (Green line)
* GPT-4 (early): 92.0 (Red line)
### Detailed Analysis
The chart presents accuracy data for various LLMs, both fine-tuned and enhanced with "+SHEPHERD".
* **LLaMA2-70B MAmmoTH:**
* Fine-tuned LLMs (Blue): 72.4%
* **LLaMA2-70B WizardMATH:**
* Fine-tuned LLMs (Blue): 81.6%
* **LLaMA2-70B MetaMATH:**
* Fine-tuned LLMs (Blue): 80.4%
* +SHEPHERD (Orange): 93.2% (Total height of the bar)
* **LLemma-34B MetaMATH*:**
* Fine-tuned LLMs (Blue): 75.8%
* +SHEPHERD (Orange): 90.9% (Total height of the bar)
* **DeepSeek-67B MetaMATH*:**
* Fine-tuned LLMs (Blue): 82.8%
* +SHEPHERD (Orange): 93.3% (Total height of the bar)
### Key Observations
* The "+SHEPHERD" enhancement consistently improves the accuracy of the LLMs.
* The DeepSeek-67B MetaMATH* with +SHEPHERD achieves the highest accuracy among the models tested, closely followed by LLaMA2-70B MetaMATH with +SHEPHERD.
* The GPT-4 models (early and 0613*) serve as benchmarks, with the +SHEPHERD enhanced models approaching or exceeding their performance.
### Interpretation
The data suggests that the "+SHEPHERD" enhancement is effective in improving the accuracy of LLMs on the GSM8K dataset. The comparison with GPT-4 models indicates that these enhanced models are competitive with state-of-the-art LLMs. The chart highlights the potential of combining fine-tuning with additional techniques like "+SHEPHERD" to achieve higher accuracy in mathematical reasoning tasks. The performance of DeepSeek-67B MetaMATH* and LLaMA2-70B MetaMATH* with +SHEPHERD is particularly noteworthy.
