simonbassey / grips

Code for our paper: "GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models"

GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models

• Authors: Archiki Prasad, Peter Hase, Xiang Zhou, and Mohit Bansal (UNC Chapel Hill)
• Paper
• Note: This is preliminary version of our code. The complete code to run all experiments in the paper will be added shortly.

Dependencies

This code is written using PyTorch and HuggingFace's Transformer repo. Running GrIPS with GPT-2 models requires access to GPUs. The search is quite light-weight (no model training involved) and therefore one GPU should suffice. On the other hand, running GrIPS with InstructGPT or GPT-3 models requires an OpenAI API key. Please add your key to the openai_key.txt file.

Installation

The simplest way to run our code is to start with a fresh environment.

conda create -n GrIPS python=3.9
source activate GrIPS
pip install -r requirements.txt

Running Search

• run_search.py contains the implementation of GrIPS.
  • By default, we use the InstructGPT Babbage model. To use a different GPT-3 model from the API change model_name in nat_inst_gpt3.py.
  • To switch to GPT-2 models, import nat_inst_gpt2.py and use an apporpriate model.
• expanded_encodeinstructions.py is a data loader file that interfaces with the task datasets provided in Natural Instructions.
• Here is an example code to run GrIPS (with default InstructGPT babbage)

python run_search.py --mode "Instruction Only" --task-idx 0 --train-seed 0 \
--num-compose 1 --num-candidates 5 --num-iters 10 --patience 2 --write-preds \
--meta-dir "logs/" --meta-name "babbage_all_edits_l_1_m_5_n_10@seed_0.txt"

Acknowledgments

We thank the authors and contributors of Callibrate Before Use, and Natural-Instructions for their public code release.

Reference

Please cite our paper if you use our dataset in your works:

@article{Prasad2022GrIPS,
  title         = {GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models},
  author        = {Archiki Prasad and Peter Hase and Xiang Zhou and Mohit Bansal},
  year          = {2022},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  eprint        = {2203.07281}
}