The rapid advancement of Large Language Models (LLMs) has demonstrated their vast potential across various domains, attributed to their extensive pretraining knowledge and exceptional generalizability. However, LLMs often encounter challenges in generating harmful content when faced with problematic prompts. To address this problem, existing work attempted to implement a gradient ascent based approach to prevent LLMs from producing harmful output. While these methods can be effective, they frequently impact the model utility in responding to normal prompts. To address this gap, we introduce Selective Knowledge negation Unlearning (SKU), a novel unlearning framework for LLMs, designed to eliminate harmful knowledge while preserving utility on normal prompts. Specifically, SKU is consisted of two stages: harmful knowledge acquisition stage and knowledge negation stage. The first stage aims to identify and acquire harmful knowledge within the model, whereas the second is dedicated to remove this knowledge. SKU selectively isolates and removes harmful knowledge in model parameters, ensuring the model’s performance remains robust on normal prompts. Our experiments conducted across various LLM architectures demonstrate that SKU identifies a good balance point between removing harmful information and preserving utility.
Create an environment from the yml file using:
conda env create -f llm_unlearn.ymlWe conduct our experiments on OPT2.7b, llama2-7b and llama2-13b model.
Since task vector would require state_dict deduction and addition between two models, we conduct our OPT model experiments on 3 A100 GPUs (80GiB), and llama model experiments on 4 A100 GPUs (80GiB). However, you don't have to run everything one time, you may break down the fine-tuning and task vector process in unlearn_harm_new.py file. We also added one new version of task vector in comment to save GPU usage, please refer to task_vector.py
We use the TruthfulQA dataset (avilable on github, one can simply use wget command to pull the data) as the normal data.
And we use PKU-SafeRLHF (avilable on Huggaingface) as the harmful data.
We can perform the unlearning by run the following command. Here we show an example of how to unlearn harmfulness learned from the pretrained OPT2.7B.
After downloading the data, create a directory called dataloader to store the data. Then, create a directory named logs in
harmful_unlearn directory to store the log files.
python unlearn_harm_new.py --bad_weight 2.5 --random_weight 2.5 --normal_weight 1
--lr 2e-4 --max_unlearn_steps 1000 --model_name=facebook/opt-2.7b
--model_save_dir=YOUR_MODEL_SAVING_PATH
--batch_size 2 --log_file=logs/opt-2.7b-unlearn_new.log
--task_vector_saving_path=YOUR_TASK_VECTOR_SAVING_PATHIn this command, bad_weight, random_weight and normal_weight are the weights to adjust each module.
lr is the learning rate.
max_unlearn_steps is the maximum number of unlearning steps.
model_name is the targeted unlearned model name.
model_save_dir is the path to save the unlearned model.
batch_size is the batch size.
log_file is the path to save the log file.
task_vector_saving_path is the path to save the task vector.
To test the efficient of the unlearned model (or some other model), you can simply run the following command:
python main.pyIn main_test.py, you can change the model name and saved model path to test different models.
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