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

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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 "[email protected]_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}
}

GitHub

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