LLM_Unlearning_Papers

Category

Parameter optimization

1. KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment. (ACL 2023)
   Lingzhi Wang, Tong Chen, Wei Yuan, Xingshan Zeng, Kam-Fai Wong, and Hongzhi Yin. [paper] [code]

2. Knowledge unlearning for mitigating privacy risks in language models. (ACL 2023)
   Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Moontae Lee, Lajanugen Logeswaran, and Minjoon Seo. [paper] [code]

3. Unlearn What You Want to Forget: Efficient Unlearning for LLMs.
   Jiaao Chen, Diyi Yang. [paper] [code]

4. Large Language Model Unlearning.
   Yuanshun Yao, Xiaojun Xu, and Yang Liu. [paper] [code]

5. DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models.
   Xinwei Wu, Junzhuo Li, Minghui Xu, Weilong Dong, Shuangzhi Wu, Chao Bian, and Deyi Xiong. [paper] [code]

6. Who's Harry Potter? Approximate Unlearning in LLMs.
   Ronen Eldan, Mark Russinovich. [paper]

7. Unlearning Bias in Language Models by Partitioning Gradients. (ACL 2023)
   Charles Yu, Sullam Jeoung, Anish Kasi, Pengfei Yu, Heng Ji. [paper] [code]

8. Make Text Unlearnable: Exploiting Effective Patterns to Protect Personal Data.
   Xinzhe Li, Ming Liu, Shang Gao. [paper]

9. Separate the Wheat from the Chaff: Model Deficiency Unlearning via Parameter-Efficient Module Operation.
   Xinshuo Hu, Dongfang Li, Zihao Zheng, Zhenyu Liu, Baotian Hu, Min Zhang. [paper]

10. Making Harmful Behaviors Unlearnable for Large Language Models.
    Xin Zhou, Yi Lu, Ruotian Ma, Tao Gui, Qi Zhang, Xuanjing Huang. [paper]

Parameter merging

1. Editing Models with Task Arithmetic.
   Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. [paper] [code]

2. Composing Parameter-Efficient Modules with Arithmetic Operations.
   Jinghan Zhang, Shiqi Chen, Junteng Liu, and Junxian He. [paper] [code]

3. Fuse to Forget: Bias Reduction and Selective Memorization through Model Fusion.
   Kerem Zaman, Leshem Choshen, Shashank Srivastava. [paper] [code]

In-context learning

1. in-context unlearning: language models as few shot unlearners.
   Martin Pawelczyk, Seth Neel, and Himabindu Lakkaraju. [paper]