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Knowledge editing aims to adjust the knowledge within large language models (LLMs) to prevent their responses from becoming obsolete or inaccurate.
Zero-shot relation extraction via reading comprehension
Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. 2017 · 2017
Earlier work this paper cites.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
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Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Ronan Le bras, Jianfeng Gao, and Yejin Choi. 2020 · 2020
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Adversarial NLI: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020 · 2020
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Modifying memories in transformer models
Chen Zhu, Ankit Singh Rawat, Manzil Zaheer, Srinadh Bhojanapalli, Daliang Li, Felix Yu, and Sanjiv Kumar. 2020 · 2020
Earlier work this paper cites.
Editing factual knowledge in language models
Nicola De Cao, Wilker Aziz, and Ivan Titov. 2021 · 2021
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Transformer feed-forward layers are key-value memories
Mor Geva, Roei Schuster, Jonathan Berant, and Omer Levy. 2021 · 2021
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Lora: Low-rank adaptation of large language models
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
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A review on language models as knowledge bases
Badr AlKhamissi, Millicent Li, Asli Celikyilmaz, Mona Diab, and Marjan Ghazvininejad. 2022 · 2022
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Knowledge neurons in pretrained transformers
Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei. 2022 · 2022
Cited alongside, same era.
Calibrating factual knowledge in pretrained language models
Qingxiu Dong, Damai Dai, Yifan Song, Jingjing Xu, Zhifang Sui, and Lei Li. 2022 · 2022
Cited alongside, same era.
Memory-assisted prompt editing to improve GPT-3 after deployment
Aman Madaan, Niket Tandon, Peter Clark, and Yiming Yang. 2022 · 2022
Cited alongside, same era.
Locating and editing factual associations in GPT
Kevin Meng, David Bau, Alex J Andonian, and Yonatan Belinkov. 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.
mgpt: Few-shot learners go multilingual
Oleh Shliazhko, Alena Fenogenova, Maria Tikhonova, Vladislav Mikhailov, Anastasia Kozlova, and Tatiana Shavrina. 2023 · 2023
Later among the works it cites.
Language anisotropic cross-lingual model editing
Yang Xu, Yutai Hou, Wanxiang Che, and Min Zhang. 2023 · 2023
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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.
Can we edit factual knowledge by in-context learning?
Ce Zheng, Lei Li, Qingxiu Dong, Yuxuan Fan, Zhiyong Wu, Jingjing Xu, and Baobao Chang. 2023 · 2023
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MQuAKE: Assessing knowledge editing in language models via multi-hop questions
Zexuan Zhong, Zhengxuan Wu, Christopher Manning, Christopher Potts, and Danqi Chen. 2023 · 2023
Later among the works it cites.
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Yuheng Chen, Pengfei Cao, Yubo Chen, Kang Liu, and Jun Zhao. 2023 · 2023
Cited alongside, same era.
A survey on in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, Lei Li, and Zhifang Sui. 2023 · 2023
Cited alongside, same era.
Aging with grace: Lifelong model editing with discrete key-value adaptors
Thomas Hartvigsen, Swami Sankaranarayanan, Hamid Palangi, Yoon Kim, and Marzyeh Ghassemi. 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.
Mass-editing memory in a transformer
Kevin Meng, Arnab Sen Sharma, Alex J Andonian, Yonatan Belinkov, and David Bau. 2023 · 2023
Cited alongside, same era.
Language models are multilingual chain-of-thought reasoners
Freda Shi, Mirac Suzgun, Markus Freitag, Xuezhi Wang, Suraj Srivats, Soroush Vosoughi, Hyung Won Chung, Yi Tay, Sebastian Ruder, Denny Zhou, Dipanjan Das, and Jason Wei. 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.
Himanshu Beniwal, Kowsik Nandagopan D, and Mayank Singh. 2024 · 2024
Closest in time.
Evaluating the ripple effects of knowledge editing in language models
Roi Cohen, Eden Biran, Ori Yoran, Amir Globerson, and Mor Geva. 2024 · 2024
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Pavel Denisov and Ngoc Thang Vu. 2024 · 2024
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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
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Mlake: Multilingual knowledge editing benchmark for large language models
Zihao Wei, Jingcheng Deng, Liang Pang, Hanxing Ding, Huawei Shen, and Xueqi Cheng. 2024 · 2024
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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. 2024 · 2024
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