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Knowledge editing has become a promising approach for efficiently and precisely updating knowledge embedded in large language models (LLMs).
Transformer feed-forward layers are key-value memories
Mor Geva, Roei Schuster, Jonathan Berant, and Omer Levy. 2020 · 2012
Earlier work this paper cites.
Attention is all you need
A Vaswani. 2017 · 2017
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Earlier work this paper cites.
Modifying memories in transformer models
Chen Zhu, Daliang Li, Felix Yu, Manzil Zaheer, Sanjiv Kumar, Srinadh Bhojanapalli, and Ankit Singh Rawat. 2021 · 2020
Earlier work this paper cites.
Editing factual knowledge in language models
N De Cao, W Aziz, and I Titov. 2021 · 2021
Earlier work this paper cites.
Measuring and improving consistency in pretrained language models
Yanai Elazar, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Eduard Hovy, Hinrich Schütze, and Yoav Goldberg. 2021 · 2021
Cited alongside, same era.
Gpt-j-6b: A 6 billion parameter autoregressive language model
Ben Wang and Aran Komatsuzaki. 2021 · 2021
Cited alongside, same era.
Knowledge neurons in pretrained transformers
Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei. 2022 · 2022
Cited alongside, same era.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
Cited alongside, same era.
Transformer-patcher: One mistake worth one neuron
Zeyu Huang, Yikang Shen, Xiaofeng Zhang, Jie Zhou, Wenge Rong, and Zhang Xiong. 2023 · 2023
Cited alongside, same era.
Pmet: Precise model editing in a transformer
Xiaopeng Li, Shasha Li, Shezheng Song, Jing Yang, Jun Ma, and Jie Yu. 2024a
Cited in the paper.
Unveiling the pitfalls of knowledge editing for large language models
Zhoubo Li, Ningyu Zhang, Yunzhi Yao, Mengru Wang, Xi Chen, and Huajun Chen. 2024b
Cited in the paper.
Locating and editing factual associations in gpt
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022a
Cited in the paper.
Mass editing memory in a transformer
Kevin Meng, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, and David Bau. 2022b
Cited in the paper.
Fast model editing at scale
Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher D Manning. 2022a
Cited in the paper.
Memory-based model editing at scale
Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D Manning, and Chelsea Finn. 2022b
Cited in the paper.
The butterfly effect of model editing: Few edits can trigger large language models collapse
Wanli Yang, Fei Sun, Xinyu Ma, Xun Liu, Dawei Yin, and Xueqi Cheng. 2024a
Cited in the paper.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
Later among the works it cites.
Easyedit: An easy-to-use knowledge editing framework for large language models
Peng Wang, Ningyu Zhang, Xin Xie, Yunzhi Yao, Bozhong Tian, Mengru Wang, Zekun Xi, Siyuan Cheng, Kangwei Liu, Guozhou Zheng, et al. 2023 · 2023
Later among the works it cites.
Editing large language models: Problems, methods, and opportunities
Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, and Ningyu Zhang. 2023 · 2023
Later among the works it cites.
Model editing can hurt general abilities of large language models
Jia-Chen Gu, Hao-Xiang Xu, Jun-Yu Ma, Pan Lu, Zhen-Hua Ling, Kai-Wei Chang, and Nanyun Peng. 2024 · 2024
Later among the works it cites.
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