Knowledge Editing for LLMs Papers

Must-read papers on knowledge editing for large language models.

🔔 News

• New Reports

  Report	Topic	PPT Resource
  IJCAI2024 tutorial	Knowledge Editing for Large Language Models	Google Drive
  CCL2024 tutorial	大语言模型知识机理、融合与编辑	BaiduPan & Google Drive
  COLING2024 tutorial	Knowledge Editing for Large Language Models	Google Drive
  北京智源大会	大语言模型知识机理与编辑问题	BaiduPan
  VALSE2024 tutorial	Knowledge Mechanism and Editing for Large Language Models	Google Drive
  AAAI2024 tutorial	Knowledge Editing for Large Language Models	Google Drive

• 2024-07-22 We release a new paper: "Knowledge Mechanisms in Large Language Models: A Survey and Perspective", which reviews how knowledge is acquired, utilized, and evolves in large language models.
• 2024-05-16 Our paper "Detoxifying Large Language Models via Knowledge Editing" has been accepted by ACL 2024.
• 2024-01-03 We release a new paper:"A Comprehensive Study of Knowledge Editing for Large Language Models" with a new benchmark KnowEdit! We are looking forward to any comments or discussions on this topic
• 2024-12-09 Our paper "Editing Language Model-based Knowledge Graph Embeddings?" has been accepted by AAAI 2024.
• 2023-11-18 We will provide a tutorial on Knowledge Editing for Large Language Models at COLING 2024.
• 2023-10-25 We will provide a tutorial on Knowledge Editing for Large Language Models at AAAI 2024.
• 2023-10-22 Our paper "Can We Edit Multimodal Large Language Models?" has been accepted by EMNLP 2023.
• 2023-10-08 Our paper "Editing Large Language Models: Problems, Methods, and Opportunities" has been accepted by EMNLP 2023.
• 2023-8-15 We release the paper "EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models."
• 2023-07 We release EasyEdit, an easy-to-use knowledge editing framework for LLMs.
• 2023-06 We will provide a tutorial on Editing Large Language Models at AACL 2023.
• 2023-05 We release a new analysis paper:"Editing Large Language Models: Problems, Methods, and Opportunities" based on this repository! We are looking forward to any comments or discussions on this topic :)
• 2022-12 We create this repository to maintain a paper list on Knowledge Editing.

🔍 Contents

• 🌟 Why Knowledge Editing?
• Keywords
• Comparisons of the different technologies
• 📜 Papers
  • Overview
  • Methods
    • Preserve Parameters
      • Memory Based
      • Additional Parameters
    • Modify Parameters
      • Finetuning
      • Meta-learning
      • Locate and edit
    • More Related Papers
  • Analysis
• 🧰 Resources
  • Benchmarks and Tasks
  • Tools
• 🎉 Contribution
  • Contributors
  • Contributing to this paper list
  • Acknowledgement
• 🚩Citation

🌟 Why Knowledge Editing?

Knowledge Editing is a compelling field of research that focuses on facilitating efficient modifications to the behavior of models, particularly foundation models. The aim is to implement these changes within a specified scope of interest without negatively affecting the model's performance across a broader range of inputs.

Keywords

Knowledge Editing has strong connections with following topics.

• Updating and fixing bugs for large language models
• Language models as knowledge base, locating knowledge in large language models
• Lifelong learning, unlearning and etc.
• Security and privacy for large language models

Comparisons of different technologies

📜 Resources

This is a collection of research and review papers of Knowledge Editing. Any suggestions and pull requests are welcome for better sharing of latest research progress.

Tutorials

Knowledge Editing for Large Language Models, AAAI 2024 Tutorial
Ningyu Zhang, Jia-Chen Gu, Yunzhi Yao, Zhen Bi, Shumin Deng. [Github] [Google Drive] [Baidu Pan]

Editing Large Language Models, AACL 2023 Tutorial
Ningyu Zhang, Yunzhi Yao, Shumin Deng. [Github] [Google Drive] [Baidu Pan]

Surveys

Knowledge Mechanisms in Large Language Models: A Survey and Perspective
Mengru Wang, Yunzhi Yao, Ziwen Xu, Shuofei Qiao, Shumin Deng, Peng Wang, Xiang Chen, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang. [paper]

A Comprehensive Study of Knowledge Editing for Large Language Models
Ningyu Zhang, Yunzhi Yao, Bozhong Tian, Peng Wang, Shumin Deng, Mengru Wang, Zekun Xi, Shengyu Mao, Jintian Zhang, Yuansheng Ni, Siyuan Cheng, Ziwen Xu, Xin Xu, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Lei Liang, Zhiqiang Zhang, Xiaowei Zhu, Jun Zhou, Huajun Chen. [paper][benchmark][code]

Editing Large Language Models: Problems, Methods, and Opportunities, EMNLP 2023 Main Conference Paper
Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, Ningyu Zhang. [paper][code]

Knowledge Editing for Large Language Models: A Survey
Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, Jundong Li. [paper]

A Survey on Knowledge Editing of Neural Networks
Vittorio Mazzia, Alessandro Pedrani, Andrea Caciolai, Kay Rottmann, Davide Bernardi. [paper]

Knowledge Unlearning for LLMs: Tasks, Methods, and Challenges
Nianwen Si, Hao Zhang, Heyu Chang, Wenlin Zhang, Dan Qu, Weiqiang Zhang. [paper]

Methods

Preserve Parameters

Memory-based

1. Memory-Based Model Editing at Scale (ICML 2022)
   Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D. Manning, Chelsea Finn. [paper] [code] [demo]

2. Fixing Model Bugs with Natural Language Patches. (EMNLP 2022)
   Shikhar Murty, Christopher D. Manning, Scott M. Lundberg, Marco Túlio Ribeiro. [paper] [code]

3. MemPrompt: Memory-assisted Prompt Editing with User Feedback. (EMNLP 2022)
   Aman Madaan, Niket Tandon, Peter Clark, Yiming Yang. [paper] [code] [page] [video]

4. Large Language Models with Controllable Working Memory.
   Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix Yu, Sanjiv Kumar. [paper]

5. Can We Edit Factual Knowledge by In-Context Learning?
   Ce Zheng, Lei Li, Qingxiu Dong, Yuxuan Fan, Zhiyong Wu, Jingjing Xu, Baobao Chang. [paper]

6. Can LMs Learn New Entities from Descriptions? Challenges in Propagating Injected Knowledge
   Yasumasa Onoe, Michael J.Q. Zhang, Shankar Padmanabhan, Greg Durrett, Eunsol Choi. [paper]

7. MQUAKE: Assessing Knowledge Editing inLanguage Models via Multi-Hop Questions
   Zexuan Zhong, Zhengxuan Wu, Christopher D. Manning, Christopher Potts, Danqi Chen.
   [paper] [code]

8. PokeMQA: Programmable knowledge editing for Multi-hop Question Answering
   Hengrui Gu, Kaixiong Zhou, Xiaotian Han, Ninghao Liu, Ruobing Wang, Xin Wang.
   [paper] [code]

9. Retrieval-augmented Multilingual Knowledge Editing
   Weixuan Wang, Barry Haddow, Alexandra Birch. [paper] [code]

10. MEMORYLLM: Towards Self-Updatable Large Language Models
    Yu Wang, Xiusi Chen, Jingbo Shang, Julian McAuley. [paper]

11. DeepEdit: Knowledge Editing as Decoding with Constraints
    Yiwei Wang,Muhao Chen,Nanyun Peng, Kai-Wei Chang. [paper]

12. Stable Knowledge Editing in Large Language Models.
    Zihao Wei,Liang Pang,Hanxing Ding,Jingcheng Deng,Huawei Shen,Xueqi Cheng. [paper]

13. Knowledge Editing on Black-box Large Language Models.
    Xiaoshuai Song, Zhengyang Wang, Keqing He, Guanting Dong, Jinxu Zhao, Weiran Xu. [paper]

14. Learning to Edit: Aligning LLMs with Knowledge Editing.
    Yuxin Jiang, Yufei Wang, Chuhan Wu, Wanjun Zhong, Xingshan Zeng, Jiahui Gao, Liangyou Li, Xin Jiang, Lifeng Shang, Ruiming Tang, Qun Liu, Wei Wang. [paper]

15. Robust and Scalable Model Editing for Large Language Models.
    Yingfa Chen, Zhengyan Zhang, Xu Han, Chaojun Xiao, Zhiyuan Liu, Chen Chen, Kuai Li, Tao Yang, Maosong Sun. [paper]

16. Retrieval-Enhanced Knowledge Editing for Multi-Hop Question Answering in Language Models.
    Yucheng Shi, Qiaoyu Tan, Xuansheng Wu, Shaochen Zhong, Kaixiong Zhou, Ninghao Liu. [paper]

17. In-Context Editing: Learning Knowledge from Self-Induced Distributions.
    Siyuan Qi, Bangcheng Yang, Kailin Jiang, Xiaobo Wang, Jiaqi Li, Yifan Zhong, Yaodong Yang, Zilong Zheng. [paper]

Additional Parameters