In the realm of Machine Learning, Large Language Models (LLMs) are making a significant impact, especially with innovative approaches like LLM2LLM. If you’re looking to enhance your LLM using novel iterative data enhancement methods as explained in the paper LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement, you’ve come to the right place. This guide will walk you through the process of reproducing the main experiments from this study using the GSM8K dataset.

Getting Started: Pre-requisites

Before diving into the coding part, ensure that you have the following at your disposal:

• A working setup for Python development.
• Access to LLaMA-2-7B model and the related dataset.

Step-by-Step Instructions

Let’s break down the process of reproducing the main experiments into straightforward steps, akin to assembling a model airplane. Each step is crucial in ensuring that your experimental setup is as effective as possible:

1. Download a copy of LLaMA-2-7B and the appropriate dataset: Ensure that you have the model ready for implementation.
2. Clone the GSM8K dataset: Open your command line or terminal and execute the following commands:

cd GSM8K
git clone https://github.com/openai/grade-school-math.git

3. Generate seed data: Run the script generate_seed_data.py and adjust the SUBSAMPLE_SPLIT value until you achieve the desired seed data.
4. Check your configuration settings: Ensure that all the settings in config.yaml are accurate to avoid errors during execution.
5. Data generation: Execute the following command to generate data:

python GSM8K/generator_data.py GSM8K/config.yaml

6. Run experiments: Navigate into your experiment folder and run the shell script:

cd your_experiment_folder
./run_all.sh

7. Analyze your results: After all iterations are complete, run the following command to compile a detailed performance breakdown:

python report_results.py --results_file_name test_0.jsonl GSM8K/grade-school-math/data/test.jsonl $EXP_FOLDER

Understanding the Code: The Model Airplane Analogy

Think of the code as the parts of a model airplane. Each code snippet represents different components that come together to create the final product:

• Clone the dataset: It’s like purchasing the package that contains the plane parts. Without it, you can’t build.
• Generating seed data: This step is akin to gluing small pieces together to form a stable structure before tackling the larger assembly.
• Running experiments: Once everything is glued and set, it’s time for you to take the model airplane and put it through flight tests – ensuring it performs well!
• Analyzing results: Finally, you check if your plane flew well, making adjustments based on your observations to ensure future flights are smoother.

Troubleshooting Your Setup

While following the above steps, you may encounter some hiccups. Here are a few troubleshooting tips:

• If the clone command fails, ensure you have Git installed and your internet connection is stable.
• If you see errors while executing Python scripts, double-check your Python version and whether all required libraries are installed.
• For any configuration issues, carefully review your config.yaml file for typos or incorrect values.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.