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Plug-and-Play Adaptation for Continuously-updated QA

This includes an implementation of "Plug-and-Play Adaptation for Continuously-updated QA"

Executing program

To train the model with source Knowledge, please use the following command

python pretrain.py --train_path $train_path --dev_path $dev_path

Here are some arguments(included but not all) which might be useful.

  • --n_gpus: the number of gpus you will use for training
  • --dataset: type of dataset, like ZsRE or nq
  • --train_path: path of train dataset
  • --dev_path: path of validation dataset
  • --init_checkpoint: Checkpoint if you want to train model from checkpoint
  • --validation_freq: The frequency of Validation(epoch)

To update the model with target Knowledge, please use the following command

python update.py --checkpoint $model_checkpoint --train_path $train_path --dev_path $dev_path --adapter $adapter --freeze_orig_param $params

Here are some arguments(included but not all) which might be useful.

  • --checkpoint: parameters of original model which will be freeze while updating
  • --adapter: type of adapter which will be exploited when updating knowledge
  • --freeze_orig_param: parameters of original model which will be freeze while updating
  • --ours_threshold: threshold when judging whether the data is from source or not

To evaluate the model, please use the following command

python eval.py --checkpoint $model_checkpoint --dev_path $dev_path --adapter $adapter

Here are some arguments(included but not all) which might be useful.

  • --checkpoint: parameters of original model which will be freeze while updating
  • --adapter: type of adapter which was exploited when updating or pretraining
  • --ours_threshold: threshold when judging whether the data is from source or not

Version History

  • 0.1
    • Initial Release

Citation

If you find this repo useful, please cite our preprint:

@article{lee2022plug,
  title={Plug-and-Play Adaptation for Continuously-updated QA},
  author={Lee, Kyungjae and Han, Wookje and Hwang, Seung-won and Lee, Hwaran and Park, Joonsuk and Lee, Sang-Woo},
  journal={arXiv preprint arXiv:2204.12785},
  year={2022}
}

License

Copyright
Copyright 2022-present SNU-NAVER Hyperscale AI Center

Acknowledgement
This research was supported by SNU-NAVER Hyperscale AI Center, and IITP grants funded by the Korea government (MSIT) [2021-0-02068 SNU AIHub, IITP-2022-2020-0-01789].