prefix-propagation

Source code for "Prefix-Propagation: Parameter-Efficient Tuning for Long Sequences" (published at ACL 2023 main conference). Codebase is based off P-tuning-v2.

Setup

We recommend creating a new conda enviornment for this project. To do so, run the following commands:

conda create --name prop python=3.10
conda activate prop

Then, install torch with conda:

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

Finally, install the rest of the requirements:

pip install -r requirements.txt

Usage

Training for prefix propagation can be done through the run.py script (make sure you have the conda environment activated). For easier configuration and easy grid hyperparameter search, see scripts in run_script. For example, to train prefix-propgation with hyperparamter search on 20-newsgroups:

bash run_script/newsgroups_longformer_propagation.sh

Wikihop Dataset Preparation

To use WikiHop, download data from http://qangaroo.cs.ucl.ac.uk/ (can be done from command line with gdown 1ytVZ4AhubFDOEL7o7XrIRIyhU8g9wvKA). Then, unzip into ./data/.

Cite

@inproceedings{li-etal-2023-prefix,
    title = "Prefix Propagation: Parameter-Efficient Tuning for Long Sequences",
    author = "Li, Jonathan  and
      Aitken, Will  and
      Bhambhoria, Rohan  and
      Zhu, Xiaodan",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-short.120",
    pages = "1408--1419",
}