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https://github.com/jinzhuoran/rwku

RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models. NeurIPS 2024
https://github.com/jinzhuoran/rwku

adversarial-attacks benchmark evaluation-framework forgetting large-language-models membership-inference-attack natural-language-processing privacy-protection right-to-be-forgotten unlearning

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RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models. NeurIPS 2024

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# RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models 





   🏠 Homepage |

   📜 Paper | 

   🤗 Dataset | 

   🚀 Installation 




### News

- 2024-09-26: 🚀🚀 Our paper has been accepted at NeurIPS D&B Track 2024.



- 2024-06-18: We released our [paper](https://arxiv.org/abs/2406.10890) titled "RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models".



- 2024-06-05: We released our [dataset](https://huggingface.co/datasets/jinzhuoran/RWKU) on the Huggingface.



### Description



RWKU is a real-world knowledge unlearning benchmark specifically designed for large language models (LLMs). This benchmark contains 200 real-world unlearning targets and 13,131 multi-level forget probes, including 3,268 fill-in-the-blank probes, 2,879 question-answer probes, and 6,984 adversarial-attack probes. RWKU is designed based on the following three key factors:



- For the **task setting**, we consider a more practical and challenging setting, similar to zero-shot knowledge unlearning. We provide only the unlearning target and the original model, without offering any forget corpus or retain corpus. In this way, it avoids secondary information leakage caused by the forget corpus and is not affected by the distribution bias of the retain corpus.

- For the **knowledge source**, we choose real-world famous people from Wikipedia as the unlearning targets and demonstrate that such popular knowledge is widely present in various LLMs through memorization quantification, making it more suitable for knowledge unlearning. Additionally, choosing entities as unlearning targets can well clearly define the unlearning boundaries.

- For the **evaluation framework**, we carefully design the **forget set** and the **retain set** to evaluate the model's capabilities from multiple real-world applications.

  - Regarding the **forget set**, we evaluate the **efficacy** of knowledge unlearning at both the **knowledge memorization** (fill-in-the-blank style) and **knowledge manipulation** (question-answer style) abilities. Specifically, we also evaluate these two abilities through **adversarial attacks** to induce forgotten knowledge in the model. We adopt four **membership inference attack** (MIA) methods for knowledge memorization on our collected MIA set. We meticulously designed nine types of adversarial-attack probes for knowledge manipulation, including _prefix injection, affirmative suffix, role playing, reverse query, and others_.

  - Regarding the **retain set**, we design a neighbor set to test the impact of _neighbor perturbation_, specifically focusing on the **locality** of unlearning. In addition, we assess the **model utility** on various downstream capabilities, including _general ability, reasoning ability, truthfulness, factuality, and fluency_.



### Installation



```bash

git clone https://github.com/jinzhuoran/RWKU.git

conda create -n rwku python=3.10

conda activate rwku

cd RWKU

pip install -r requirements.txt

```



### Dataset Download and Processing



One way is to load the dataset from [Huggingface](https://huggingface.co/datasets/jinzhuoran/RWKU) and preprocess it.

```bash

cd process

python data_process.py

```



Another way is to download the processed dataset directly from [Google Drive](https://drive.google.com/file/d/1ukWg-T3GPvqpyW7058vNyRWdXuQHRJPb/view?usp=sharing).

```bash

cd LLaMA-Factory/data

bash download.sh

```



### Unlearning Target



RWKU includes 200 famous people from [The Most Famous All-time People Rank](https://today.yougov.com/ratings/international/fame/all-time-people/all), such as Stephen King, Warren Buffett, Taylor Swift, etc.

We demonstrate that such popular knowledge is widely present in various LLMs through memorization quantification, making it more suitable for unlearning.

```python

from datasets import load_dataset

forget_target = load_dataset("jinzhuoran/RWKU", 'forget_target')['train'] # 200 unlearning targets

```



### Evaluation Framework



RWKU mainly consists of four subsets, including forget set, neighbor set, MIA set and utility set.

![Evaluation Framework.](file/framework.png)



#### Forget Set



```python

from datasets import load_dataset

forget_level1 = load_dataset("jinzhuoran/RWKU", 'forget_level1')['test'] # forget knowledge memorization probes

forget_level2 = load_dataset("jinzhuoran/RWKU", 'forget_level2')['test'] # forget knowledge manipulation probes

forget_level3 = load_dataset("jinzhuoran/RWKU", 'forget_level3')['test'] # forget adversarial attack probes

```



#### Neighbor Set



```python

from datasets import load_dataset

neighbor_level1 = load_dataset("jinzhuoran/RWKU", 'neighbor_level1')['test'] # neighbor knowledge memorization probes

neighbor_level2 = load_dataset("jinzhuoran/RWKU", 'neighbor_level2')['test'] # neighbor knowledge manipulation probes

```



#### MIA Set



```python

from datasets import load_dataset

mia_forget = load_dataset("jinzhuoran/RWKU", 'mia_forget') # forget member set

mia_retain = load_dataset("jinzhuoran/RWKU", 'mia_retain') # retain member set

```



#### Utility Set



```python

from datasets import load_dataset

utility_general = load_dataset("jinzhuoran/RWKU", 'utility_general') # general ability

utility_reason = load_dataset("jinzhuoran/RWKU", 'utility_reason') # reasoning ability

utility_truthfulness = load_dataset("jinzhuoran/RWKU", 'utility_truthfulness') # truthfulness

utility_factuality = load_dataset("jinzhuoran/RWKU", 'utility_factuality') # factuality

utility_fluency = load_dataset("jinzhuoran/RWKU", 'utility_fluency') # fluency

```



### Supported Unlearning Methods

- **In-Context Unlearning (ICU)**: We use specific instructions to make the model behave as if it has forgotten the target knowledge, without actually modifying the model parameters.

- **Gradient Ascent (GA)**: In contrast to the gradient descent during the pre-training phase, we maximize the negative log-likelihood loss on the forget corpus. This approach aims to steer the model away from its initial predictions, facilitating the process of unlearning. 

- **Direct Preference Optimization (DPO)**: We apply preference optimization to enable the model to generate incorrect target knowledge. DPO requires positive and negative examples to train the model. For the positive example, we sample it from the counterfactual corpus, which consists of intentionally fabricated descriptions generated by the model about the target. For the negative example, we sample it from the synthetic forget corpus. 

- **Negative Preference Optimization (NPO)**: NPO is a simple drop-in fix of the GA loss. Compared to DPO, NPO retains only the negative examples without any positive examples. 

- **Rejection Tuning (RT)**: First, we have the model generate some questions related to the unlearning targets, then replace its responses with “I do not know the answer.”. Then, we use this refusal data to fine-tune the model so that it can reject questions related to the target.



### Forget Corpus Generation

We have provided the forget corpus for both [Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) and [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) to facilitate reproducibility.

```python

from datasets import load_dataset

train_positive_llama3 = load_dataset("jinzhuoran/RWKU", 'train_positive_llama3')['train'] # For GA and NPO

train_pair_llama3 = load_dataset("jinzhuoran/RWKU", 'train_pair_llama3')['train'] # For DPO

train_refusal_llama3 = load_dataset("jinzhuoran/RWKU", 'train_refusal_llama3')['train'] # For RT



train_positive_phi3 = load_dataset("jinzhuoran/RWKU", 'train_positive_phi3')['train'] # For GA and NPO

train_pair_phi3 = load_dataset("jinzhuoran/RWKU", 'train_pair_phi3')['train'] # For DPO

train_refusal_phi3 = load_dataset("jinzhuoran/RWKU", 'train_refusal_phi3')['train'] # For RT

```



Additionally, you can construct your own forget corpus to explore new methods and models.

We have included our generation script for reference. Please feel free to explore better methods for generating forget corpus.

```bash

cd generation

python pair_generation.py # For GA, DPO and NPO

python question_generation.py # For RT

```



### Evaluating Models 



To evaluate the model original performance before unlearning.

```bash

cd LLaMA-Factory/scripts

bash run_original.sh

```



### Unlearning Models



We adapt [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) to train the model. 

We provide several scripts to run various unlearning methods.



#### Single-sample Unlearning Setting



To run the In-Context Unlearning (ICU) method on Llama-3-8B-Instruct.

```bash

cd LLaMA-Factory

bash scripts/full/run_icu.sh

```

To run the Gradient Ascent (GA) method on Llama-3-8B-Instruct.

```bash

cd LLaMA-Factory

bash scripts/full/run_ga.sh

```

To run the Direct Preference Optimization (DPO) method on Llama-3-8B-Instruct.

```bash

cd LLaMA-Factory

bash scripts/full/run_dpo.sh

```

To run the Negative Preference Optimization (NPO) method on Llama-3-8B-Instruct.

```bash

cd LLaMA-Factory

bash scripts/full/run_npo.sh

```

To run the Rejection Tuning (RT) method on Llama-3-8B-Instruct.

```bash

cd LLaMA-Factory

bash scripts/full/run_rt.sh

```



#### Batch-sample Unlearning Setting



To run the In-Context Unlearning (ICU) method on Llama-3-8B-Instruct.

```bash

cd LLaMA-Factory

bash scripts/batch/run_icu.sh

```

To run the Gradient Ascent (GA) method on Llama-3-8B-Instruct.

```bash

cd LLaMA-Factory

bash scripts/batch/run_ga.sh

```

To run the Direct Preference Optimization (DPO) method on Llama-3-8B-Instruct.

```bash

cd LLaMA-Factory

bash scripts/batch/run_dpo.sh

```

To run the Negative Preference Optimization (NPO) method on Llama-3-8B-Instruct.

```bash

cd LLaMA-Factory

bash scripts/batch/run_npo.sh

```

To run the Rejection Tuning (RT) method on Llama-3-8B-Instruct.

```bash

cd LLaMA-Factory

bash scripts/batch/run_rt.sh

```



#### LoRA Unlearning Setting



Please set `--finetuning_type lora` and `--lora_target q_proj,v_proj`.



#### Partial-layer Unlearning Setting



Please set `--train_layers 0-4`.



### Experimental Results

Results of main experiment on LLaMA3-Instruct (8B).

![Results of main experiment on LLaMA3-Instruct (8B).](file/result_llama.jpg)



Results of main experiment on Phi-3 Mini-4K-Instruct (3.8B).



![Results of main experiment on Phi-3 Mini-4K-Instruct (3.8B).](file/result_phi.jpg)



### Citation



If you find our codebase and dataset beneficial, please cite our work:



```bibtex

@misc{jin2024rwku,

    title={RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models},

    author={Zhuoran Jin and Pengfei Cao and Chenhao Wang and Zhitao He and Hongbang Yuan and Jiachun Li and Yubo Chen and Kang Liu and Jun Zhao},

    year={2024},

    eprint={2406.10890},

    archivePrefix={arXiv},

    primaryClass={cs.CL}

}

```



### Other Related Projects



- [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)

- [MIMIR](https://github.com/iamgroot42/mimir)

- [TOFU](https://github.com/locuslab/tofu)

- [repeng](https://github.com/vgel/repeng)