https://github.com/squeezeailab/llm2llm

[ACL 2024] LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement
https://github.com/squeezeailab/llm2llm

data-augmentation llama llama2 llm llms natural-language-processing nlp synthetic-dataset-generation transformer

Last synced: about 2 months ago
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[ACL 2024] LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement

• Host: GitHub
• URL: https://github.com/squeezeailab/llm2llm
• Owner: SqueezeAILab
• License: mit
• Created: 2024-03-21T08:59:17.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-03-25T05:33:34.000Z (10 months ago)
• Last Synced: 2024-10-18T23:12:41.052Z (3 months ago)
• Topics: data-augmentation, llama, llama2, llm, llms, natural-language-processing, nlp, synthetic-dataset-generation, transformer
• Language: Python
• Homepage: https://arxiv.org/abs/2403.15042
• Size: 209 KB
• Stars: 151
• Watchers: 6
• Forks: 10
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement [[Paper](https://arxiv.org/abs/2403.15042)]

![Thumbnail](figs/llm2llm.png)

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},

}