README.md

Instruction Matters, a Simple yet Effective Task Selection Approach in Instruction Tuning for Specific Tasks

This is the official github repository for 'Instruction Matters, a Simple yet Effective Task Selection Approach in Instruction Tuning for Specific Tasks' [EMNLP 2024].

Citation:

@misc{lee2024instructionmatterssimpleeffective,
      title={Instruction Matters, a Simple yet Effective Task Selection Approach in Instruction Tuning for Specific Tasks}, 
      author={Changho Lee and Janghoon Han and Seonghyeon Ye and Stanley Jungkyu Choi and Honglak Lee and Kyunghoon Bae},
      year={2024},
      eprint={2404.16418},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2404.16418}, 
}

0. Install Dependencies

conda create -n insta python=3.10
conda activate insta

# install torch with the correct cuda version, check nvcc --version
pip install torch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 --index-url https://download.pytorch.org/whl/cu118
# install Hugging Face Libraries
pip install "transformers==4.37.0" "datasets==2.19.1" "accelerate==0.25.0" "evaluate==0.4.0" --upgrade
# install deepspeed and ninja for jit compilations of kernels
pip install "deepspeed==0.9.3" ninja --upgrade
# install additional dependencies needed for training
pip install rouge-score nltk py7zr tensorboard scikit-learn
pip install sentencepiece
pip install wandb
pip install absl-py

git clone https://github.com/CHLee0801/INSTA.git
cd INSTA

1. Download Data

First, download the P3, BigBench, BBH datasets.

gdown https://drive.google.com/uc?id=1UvoA4Ri4w7oPnmtYDchGaOwT2Q5oSwKi
jar xvf data.zip

Second, process NIV2 datasets.

cd data/natural_instructions
git clone https://github.com/allenai/natural-instructions.git
python generate_dataset.py train
python generate_dataset_pos.py train
python generate_dataset_pos.py test

2. Train any LMs in Huggingface

bash run.sh

3. Evaluate any LMs in Huggingface

Run the inference.sh file to evaluate! You can either choose task cluster(s) to evaluate or specific task(s) to evaluate.

bash inference.sh