This is the implementation of Control Prefixes for Parameter-efficient Text Generation
This technique extends Prefix-Tuning, a parameter-efficient technique that tunes prompts at every layers of the transformer and keeps the base LM fixed. Control Prefixes was the first paper to prefix-tune T5 and therefore show how powerful this architecture can be for Structure knowledge graph tasks such as Data-to-Text.
Control Prefixes or LayerControl extends the prefix-tuning framework by having multiple control prefixes for data-point level information. This can inform the model at every layer of attribute-level information and fits into the prefix-tuning framework by sharing the same reparameterizations. Control Prefixes outperforms other methods of conditioning on attribute information that only operate on the token level.
As a result the technique is state-of-the-art on several datasets:
Data-to-Text:
Abstractive Text Summarization:
Text Simplification:
Jordan Clive(jordan.clive19@imperial.ac.uk). If you have any questions or ideas/improvements please contact me.
git clone https://github.com/jordiclive/ControlPrefixes.git
cd ControlPrefixes
pip install .
unzip src/data.zip
unzip src/datatotext/utils.zip
docker run --gpus '"all"' --rm -it jordiclive/controlprefixes:main-latest
Data-to-Text datasets with conditional data-point attribute information is provided at src/data/. For XSum, sample files are provided.
Each model config is contained in the config folder. E.g. For data-to-text at src/datatotext/configs/. Edit the data and output directory paths, gpus. To use distributed training refer to the Pytorch-Lightning docs.
To run training, e.g. for the original 2017 WebNLG.
$ cd src/datatotext
$ python read_yaml.py configs/webnlg17_config.yaml
Apache License
@article{DBLP:journals/corr/abs-2110-08329,
author = {Jordan Clive and
Kris Cao and
Marek Rei},
title = {Control Prefixes for Text Generation},
journal = {CoRR},
volume = {abs/2110.08329},
year = {2021},
url = {https://arxiv.org/abs/2110.08329},
eprinttype = {arXiv},
eprint = {2110.08329},
timestamp = {Fri, 22 Oct 2021 13:33:09 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2110-08329.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{DBLP:journals/corr/abs-2101-00190,
author = {Xiang Lisa Li and
Percy Liang},
title = {Prefix-Tuning: Optimizing Continuous Prompts for Generation},
journal = {CoRR},
volume = {abs/2101.00190},
year = {2021},
url = {https://arxiv.org/abs/2101.00190},
archivePrefix = {arXiv},
eprint = {2101.00190},
timestamp = {Thu, 21 Jan 2021 14:42:30 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2101-00190.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}