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Knowledge editing is a rising technique for efficiently updating factual knowledge in large language models (LLMs) with minimal alteration of parameters.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
Earlier work this paper cites.
Zero-shot relation extraction via reading comprehension
Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. 2017 · 2017
Earlier work this paper cites.
SAMSum corpus: A human-annotated dialogue dataset for abstractive summarization
Bogdan Gliwa, Iwona Mochol, Maciej Biesek, and Aleksander Wawer. 2019 · 2019
Earlier work this paper cites.
Natural questions: A benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
Earlier work this paper cites.
Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Ronan Le bras, Jianfeng Gao, and Yejin Choi. 2020 · 2020
Earlier work this paper cites.
Editing factual knowledge in language models
Nicola De Cao, Wilker Aziz, and Ivan Titov. 2021 · 2021
Earlier work this paper cites.
Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021 · 2021
Earlier work this paper cites.
Transformer feed-forward layers are key-value memories
Mor Geva, Roei Schuster, Jonathan Berant, and Omer Levy. 2021 · 2021
Earlier work this paper cites.
GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Ben Wang and Aran Komatsuzaki. 2021 · 2021
Earlier work this paper cites.
Knowledge neurons in pretrained transformers
Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei. 2022 · 2022
Earlier work this paper cites.
Calibrating factual knowledge in pretrained language models
Qingxiu Dong, Damai Dai, Yifan Song, Jingjing Xu, Zhifang Sui, and Lei Li. 2022 · 2022
Earlier work this paper cites.
Transformer feed-forward layers build predictions by promoting concepts in the vocabulary space
Mor Geva, Avi Caciularu, Kevin Wang, and Yoav Goldberg. 2022 · 2022
Earlier work this paper cites.
Locating and editing factual associations in gpt
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022 · 2022
Cited alongside, same era.
Evaluating the ripple effects of knowledge editing in language models
Roi Cohen, Eden Biran, Ori Yoran, Amir Globerson, and Mor Geva. 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.
Does localization inform editing? surprising differences in causality-based localization vs. knowledge editing in language models
Peter Hase, Mohit Bansal, Been Kim, and Asma Ghandeharioun. 2023 · 2023
Cited alongside, same era.
Detecting edit failures in large language models: An improved specificity benchmark
Jason Hoelscher-Obermaier, Julia Persson, Esben Kran, Ioannis Konstas, and Fazl Barez. 2023 · 2023
Cited alongside, same era.
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
Later among the works it cites.
MQuAKE: Assessing knowledge editing in language models via multi-hop questions
Zexuan Zhong, Zhengxuan Wu, Christopher D Manning, Christopher Potts, and Danqi Chen. 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
Closest in time.
Linearity of relation decoding in transformer language models
Evan Hernandez, Arnab Sen Sharma, Tal Haklay, Kevin Meng, Martin Wattenberg, Jacob Andreas, Yonatan Belinkov, and David Bau. 2024 · 2024
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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. 2023 · 2023
Cited alongside, same era.
A survey on knowledge editing of neural networks
Vittorio Mazzia, Alessandro Pedrani, Andrea Caciolai, Kay Rottmann, and Davide Bernardi. 2023 · 2023
Cited alongside, same era.
Mass editing memory in a transformer
Kevin Meng, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, and David Bau. 2023 · 2023
Cited alongside, same era.
DyLoRA: Parameter-efficient tuning of pre-trained models using dynamic search-free low-rank adaptation
Mojtaba Valipour, Mehdi Rezagholizadeh, Ivan Kobyzev, and Ali Ghodsi. 2023 · 2023
Cited alongside, same era.
Editing large language models: Problems, methods, and opportunities
Yunzhi Yao, Peng Wang, Bo Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, and Ningyu Zhang. 2023 · 2023
Cited alongside, same era.
Model editing at scale leads to gradual and catastrophic forgetting
Akshat Gupta, Anurag Rao, and Gopala Anumanchipalli. 2024a
Cited in the paper.
Wenyue Hua, Jiang Guo, Mingwen Dong, Henghui Zhu, Patrick Ng, and Zhiguo Wang. 2024 · 2024
Closest in time.
Unveiling the pitfalls of knowledge editing for large language models
Zhoubo Li, Ningyu Zhang, Yunzhi Yao, Mengru Wang, Xi Chen, and Huajun Chen. 2024 · 2024
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Is it possible to edit large language models robustly?
Xinbei Ma, Tianjie Ju, Jiyang Qiu, Zhuosheng Zhang, hai zhao, lifeng Liu, and Yulong Wang. 2024 · 2024
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Massive editing for large language models via meta learning
Chenmien Tan, Ge Zhang, and Jie Fu. 2024 · 2024
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Deepedit: Knowledge editing as decoding with constraints
Yiwei Wang, Muhao Chen, Nanyun Peng, and Kai wei Chang. 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
Closest in time.
Melo: Enhancing model editing with neuron-indexed dynamic lora
Lang Yu, Qin Chen, Jie Zhou, and Liang He. 2024 · 2024
Closest in time.