https://github.com/Lingzhi-WANG/KGAUnlearn

https://github.com/Lingzhi-WANG/KGAUnlearn

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• Host: GitHub
• URL: https://github.com/Lingzhi-WANG/KGAUnlearn
• Owner: Lingzhi-WANG
• License: mit
• Created: 2023-05-09T03:30:22.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2023-09-10T06:51:17.000Z (almost 2 years ago)
• Last Synced: 2024-10-29T04:34:31.863Z (8 months ago)
• Language: Python
• Size: 24.4 KB
• Stars: 16
• Watchers: 1
• Forks: 1
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-llm-unlearning - GitHub

README

        
#  KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment (ACL 2023)

[[Paper]](https://aclanthology.org/2023.acl-long.740.pdf) 

## Requirement:

* Python: 3.6+

* Pytorch: 1.8.1+

## Data Preparation:

Classification: LEDGAR (Please refer to the [Format](https://github.com/dtuggener/LEDGAR_provision_classification/))

Translation: IWSLT De-En (Format: xxx.de and xxx.en two files, parallel lines)

Response generation: PersonaChat (Format: xxx.src and xxx.tgt, same as translation)

## Usage Examples:

### Classification:

Train:

```

CUDA_VISIBLE_DEVICES=0 python run_classification.py --data [data-path] \

    --do_train \

    --mode train-forget [or train-new] \

    --file_as_new [Indices file for new] \

    --file_removals [Indices file for forget] \

    --save_path [model save file name] \

    --batch_size 32 \

    --sample_ratio 0.05 \

    --learning_rate 2e-5 \

    --warmup_steps 500 

```

Unlearn:

```

CUDA_VISIBLE_DEVICES=0 python kga_classification.py \

    --data [data path] \

    --do_unlearn \

    --file_as_new [Indices file for new] \

    --file_removals [Indices file for forget] \ 

    --save_path [model save path] \

    --model_path [original model path] \

    --new_model_path [new model path] \

    --forget_model_path [forget model path] \

    --batch_size 32 \

    --sample_ratio 0.3 \ 

    --learning_rate 2e-5 \

    --warmup_steps 500 \

    --retain_loss_ratio 0.1 \ 

    --inner_step 10 \ 

    --print_loss 200 \ 

    --eval_update 1000 \ 

    --save_update 5000

```

### Translation:

Train:

```

CUDA_VISIBLE_DEVICES=0 python run_translation.py \

    --output_dir [model save path] \

    --model_type marian \

    --model_checkpoint opus_mt_de_en \

    --train_file [train data path] \

    --dev_file [dev data path] \

    --do_train \

    --batch_size 32 \

    --update_freq 16 \

    --learning_rate 5e-4 \

    --num_train_epochs 50 \

    --warmup_steps 4000 \

    --weight_decay 0.0001 \

    --dropout 0.3 \

    --lr_schedule "inverse_sqrt" \

    --beam 5 \

    --source de --target en

```

Unlearn:

```

CUDA_VISIBLE_DEVICES=0 python kga_translation.py \

    --output_dir [model save path] \

    --new_model_dir [new model path] \

    --forget_model_dir [forget model path] \

    --train_model_dir [original model path] \

    --model_type marian \

    --model_checkpoint opus_mt_de_en \

    --train_file [train data path] \

    --dev_file [dev data path] \

    --forget_file [forget data path] \

    --new_file [new data path] \

    --do_unlearn \

    --retain_loss_ratio 0.3 \

    --batch_size 16 \

    --update_freq 8 \

    --learning_rate 2e-5 \

    --num_train_updates 5000 \

    --warmup_steps 1000 \

    --weight_decay 0.0001 \

    --lr_schedule "inverse_sqrt" \

    --beam 5 \

    --source de --target en

```

### Response Generation:

Train:

```

CUDA_VISIBLE_DEVICES=0 python run_generation.py \

    --output_dir [model save path] \

    --model_checkpoint bart-base \

    --train_file [train data path] \

    --dev_file [dev data path] \

    --do_train \

    --batch_size 16 \

    --update_freq 2 \

    --learning_rate 2e-4 \

    --num_train_epochs 100 \

    --warmup_steps 4000 \

    --weight_decay 0.0001 \

    --dropout 0.3 \

    --lr_schedule "inverse_sqrt" \

    --beam 5

```

Unlearn:

```

CUDA_VISIBLE_DEVICES=0 python kga_generation.py \

    --output_dir [model save path] \

    --new_model_dir [new model path] \

    --forget_model_dir [forget model path] \

    --train_model_dir [original model path] \

    --model_checkpoint bart-base \

    --train_file [train data path] \

    --dev_file [dev data path] \

    --forget_file [forget data path] \

    --new_file [new data path] \

    --do_unlearn \

    --retain_loss_ratio 0.3 \

    --batch_size 16 \

    --update_freq 2 \

    --learning_rate 5e-5 \

    --num_train_updates 12000 \

    --num_train_epochs 50 \

    --stop_value 0.05 \

    --warmup_steps 1000 \

    --weight_decay 0.0001 \

    --lr_schedule "inverse_sqrt" \

    --beam 5

```

## Reference

If you find our paper helpful and use this code, please cite our publication at ACL 2023. 

```

@inproceedings{wang-etal-2023-kga,

    title = "{KGA}: A General Machine Unlearning Framework Based on Knowledge Gap Alignment",

    author = "Wang, Lingzhi  and

      Chen, Tong  and

      Yuan, Wei  and

      Zeng, Xingshan  and

      Wong, Kam-Fai  and

      Yin, Hongzhi",

    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",

    month = jul,

    year = "2023",

    address = "Toronto, Canada",

    publisher = "Association for Computational Linguistics",

    url = "https://aclanthology.org/2023.acl-long.740",

    doi = "10.18653/v1/2023.acl-long.740",

    pages = "13264--13276",

}

```