SimNPO-Unlearned Model on Task "MUSE - News"
Model Details
- Unlearning:
- Task: 🤗datasets/muse-bench/MUSE-News
- Method: SimNPO
- Origin Model: 🤗muse-bench/MUSE-news_target
- Code Base: github.com/OPTML-Group/Unlearn-Simple
- Research Paper: "Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning"
Unlearning Algorithm
This model uses the SimNPO unlearning algorithm with the following optimization objective:
$$\ell_{SimNPO}(\mathbf{\theta}) = \mathbb{E}{(x, y) \in \mathcal{D}f}\left[-\frac{2}{\beta}\log\sigma\left(-\frac{\beta}{|y|}\log\pi{\mathbf{\theta}}(y|x) - \gamma\right)\right] + \lambda \mathbb{E}{(x, y) \in \mathcal{D}r}[-\log\pi{\mathbf{\theta}} (y|x)]$$
Unlearning hyper-parameters:
- Learning Rate:
1e-5 - beta:
0.7 - lambda:
1.0 - gamma:
3.0
Loading the Model
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("OPTML-Group/SimNPO-MUSE-News-llama-2-7b", torch_dtype=torch.bfloat16, device_map='auto')
Evaluation Results
| VerbMem Df | KnowMem Df | PrivLeak | KnowMem Dr | |
|---|---|---|---|---|
| Origin | 58.29 | 62.93 | -98.71 | 54.31 |
| Retrain | 20.75 | 33.32 | 0.00 | 53.79 |
| NPO | 0.00 | 56.93 | 56.93 | 108.91 |
| SimNPO | 12.90 | 47.09 | 11.90 | 40.31 |
Citation
If you use this model in your research, please cite:
@article{fan2024simplicity,
title={Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning},
author={Fan, Chongyu and Liu, Jiancheng and Lin, Licong and Jia, Jinghan and Zhang, Ruiqi and Mei, Song and Liu, Sijia},
journal={arXiv preprint arXiv:2410.07163},
year={2024}
}
Reporting Issues
Reporting issues with the model: github.com/OPTML-Group/Unlearn-Simple