LearnFromHumanEdit

Installation

If using conda, you can get this to work as follows:

conda create -n salt python=3.8
conda activate salt

We have experimented with 11.7 and 10.2 cuda version, but this release should work with more recent versions as well.

conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=10.2 -c pytorch

or

conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia 

Install other packages:

conda install -c conda-forge matplotlib
conda install -c conda-forge spacy
conda install -c conda-forge scipy
python -m spacy download en_core_web_sm
pip install nltk
pip install ipdb
pip install rouge
pip install rouge-score
pip install trl
pip install minineedle
pip install nltk

pip install datasets
pip install transformers

If you want to use qlora for llm:

pip install -q -U bitsandbytes 
pip install -q -U git+https://github.com/huggingface/peft.git 
pip install -q -U git+https://github.com/huggingface/accelerate.git

Run the trainer

python DPO_trainer.py
python SFT_trainer.py
python SALT_trainer.py

Run Synthetic Data Generation

python SyntheticData.py

Instructions for Synthetic Data Generation

Use the above script for the generation of synthetic data of two types:

1. High to Low (H2L): where the chosen summary is the reference summary & rejected summary is the LLM hallucinated summary.
2. Low to High (L2H): where the rejected summary is the pre-trained model-generated summary & chosen summary is the factually improved summary.

Make the following changes based on different synthetic data generation settings:

1. Add the OpenAI API key in the openai_api_key variable.
2. Update the pre-trained model checkpoint path in model_checkpoint variable for low to high (L2H) synthetic generation.
3. Update the OpenAI model type in gpt_model_type variable. This model is used to generate hallucinated and factually improved summaries.
   • gpt_model_type: gpt-3.5-turbo-0613 for using GPT-3.5 Turbo
   • gpt_model_type: gpt-4-0613 for using GPT-4
4. Update the synthetic data generation type in synthetic_data_type variable.
   • synthetic_data_type: H2L for High to Low synthetic data.
   • synthetic_data_type: L2H for Low to High synthetic data.
5. Update data_files variable to update the path for the base dataset.
6. Use num_samples to control the size of the synthetic dataset.

TODO

• Add after-visit-summary datasets (L2H, H2L) * (GPT-3.5-turbo, GPT4) --> every dataset has around 5,000 data points
• Run synthetic imitation edit generation codes on the doctor-patient-conversation-to-note synthetic dataset (https://github.com/believewhat/Dr.NoteAid/tree/main)

Citation

@article{yao2023improving,
  title={Improving Summarization with Human Edits},
  author={Yao, Zonghai and Schloss, Benjamin J and Selvaraj, Sai P},
  journal={arXiv preprint arXiv:2310.05857},
  year={2023}
}

@article{mishra2023synthetic,
  title={Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarization},
  author={Mishra, Prakamya and Yao, Zonghai and Chen, Shuwei and Wang, Beining and Mittal, Rohan and Yu, Hong},
  journal={arXiv preprint arXiv:2310.20033},
  year={2023}
}