LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement [Paper]

This is the code for the LLM2LLM paper.

Reproducing Main Experiments

We have provided code required to reproduce our main experiments for GSM8K. Instructions for other datasets will be uploaded soon.

1. Download a copy of LLaMA-2-7B, and the appropriate dataset
2. Clone the GSM8K dataset by running

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

3. Run generate_seed_data.py and adjust SUBSAMPLE_SPLIT to get seed data.
4. Ensure that all settings in config.yaml are accurate
5. Run python GSM8K/generator_data.py GSM8K/config.yaml
6. cd into your experiment folder and run ./run_all.sh
7. After all of the iterations have finished, run

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

   to get a detailed breakdown of the performance of the model at each iteration.

This will produce an output folder that contains all the data and model checkpoints.

Roadmap

We are planning on adding the code required to reproduce our experiments on other datasets.

Citation

LLM2LLM has been developed as part of the following paper. We would appreciate if you would please cite this paper if you found this library useful for your work:

@article{lee2024llm2llm,
      title={LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement}, 
      author={Lee, Nicholas and Wattanawong, Thanakul and Kim, Sehoon and Mangalam, Karttikeya and Shen, Sheng and Anumanchipali, Gopala and Mahoney, Michael W and Keutzer, Kurt and Gholami, Amir},
      journel={arXiv},
      year={2024},
}