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Knowledge representation has been a central aim of AI since its inception.
Language Models as Knowledge Bases?
Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H. Miller, and Sebastian Riedel. 2019b · 1909
Earlier work this paper cites.
Language Models are Few-Shot Learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2005
Earlier work this paper cites.
DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing . 564–573
Wenhan Xiong, Thien Hoang, and William Yang Wang. 2017 · 2017
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Commonsense knowledge aware conversation generation with graph attention.. In IJCAI . 4623–4629
Hao Zhou, Tom Young, Minlie Huang, Haizhou Zhao, Jingfang Xu, and Xiaoyan Zhu. 2018 · 2018
Earlier work this paper cites.
Language Models as Knowledge Bases?. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019 , Kentaro Inui, Jing Jiang, Vincent Ng, and Xiaojun Wan (Eds.). Association for Computational Linguistics, 2463–2473
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick S. H. Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander H. Miller. 2019a · 2019
Earlier work this paper cites.
Kgat: Knowledge graph attention network for recommendation. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining . 950–958
Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019 · 2019
Earlier work this paper cites.
A Survey on Deep Learning for Named Entity Recognition
Jing Li, Aixin Sun, Jianglei Han, and Chenliang Li. 2022 · 2020
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Semantic guided and response times bounded top-k similarity search over knowledge graphs. In 2020 IEEE 36th International Conference on Data Engineering (ICDE) . IEEE, 445–456
Yuxiang Wang, Arijit Khan, Tianxing Wu, Jiahui Jin, and Haijiang Yan. 2020 · 2020
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Pre-trained models: Past, present and future
Xu Han, Zhengyan Zhang, Ning Ding, Yuxian Gu, Xiao Liu, Yuqi Huo, Jiezhong Qiu, Yuan Yao, Ao Zhang, Liang Zhang, Wentao Han, Minlie Huang, Qin Jin, Yanyan Lan, Yang Liu, Zhiyuan Liu, Zhiwu Lu, Xipeng Qiu, Ruihua Song, Jie Tang, Ji-Rong Wen, Jinhui Yuan, Wayne Xin Zhao, and Jun Zhu. 2021 · 2021
Earlier work this paper cites.
A survey on knowledge graphs: Representation, acquisition, and applications
Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, and S Yu Philip. 2021 · 2021
Earlier work this paper cites.
A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event / Montreal, Canada, 19-27 August 2021 , Zhi-Hua Zhou (Ed.). ijcai.org, 4483–4491
Yunshi Lan, Gaole He, Jinhao Jiang, Jing Jiang, Wayne Xin Zhao, and Ji-Rong Wen. 2021 · 2021
Earlier work this paper cites.
Conditional graph attention networks for distilling and refining knowledge graphs in recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 1834–1843
Ke Tu, Peng Cui, Daixin Wang, Zhiqiang Zhang, Jun Zhou, Yuan Qi, and Wenwu Zhu. 2021 · 2021
Earlier work this paper cites.
Curriculum-meta learning for order-robust continual relation extraction. In Proceedings of the AAAI conference on artificial intelligence , Vol. 35. 10363–10369
Tongtong Wu, Xuekai Li, Yuan-Fang Li, Gholamreza Haffari, Guilin Qi, Yujin Zhu, and Guoqiang Xu. 2021 · 2021
Earlier work this paper cites.
OntoProtein: Protein Pretraining With Gene Ontology Embedding. In International Conference on Learning Representations
Ningyu Zhang, Zhen Bi, Xiaozhuan Liang, Siyuan Cheng, Haosen Hong, Shumin Deng, Qiang Zhang, Jiazhang Lian, and Huajun Chen. 2021 · 2021
Earlier work this paper cites.
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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A new Knowledge Inference Approach Based on Multi-headed Attention Mechanism
Yichao Cai, Qingyu Yang, Wei Chen, Ge Wang, Taian Liu, and Xinying Liu. 2022 · 2022
Earlier work this paper cites.
KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction. In Proceedings of the ACM Web Conference 2022 (WWW ’22) . ACM
Xiang Chen, Ningyu Zhang, Xin Xie, Shumin Deng, Yunzhi Yao, Chuanqi Tan, Fei Huang, Luo Si, and Huajun Chen. 2022 · 2022
Earlier work this paper cites.
Locating and Editing Factual Associations in GPT
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022a · 2022
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Mass-editing memory in a transformer
Kevin Meng, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, and David Bau. 2022b · 2022
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Efficiently embedding dynamic knowledge graphs
Tianxing Wu, Arijit Khan, Melvin Yong, Guilin Qi, and Meng Wang. 2022 · 2022
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Chain-of-Verification Reduces Hallucination in Large Language Models
Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, and Jason Weston. 2023 · 2023
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Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors. In Advances in Neural Information Processing Systems
Thomas Hartvigsen, Swami Sankaranarayanan, Hamid Palangi, Yoon Kim, and Marzyeh Ghassemi. 2023 · 2023
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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
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Large language models and knowledge graphs: Opportunities and challenges
Jeff Z Pan, Simon Razniewski, Jan-Christoph Kalo, Sneha Singhania, Jiaoyan Chen, Stefan Dietze, Hajira Jabeen, Janna Omeliyanenko, Wen Zhang, Matteo Lissandrini, et al · 2023
Zhuoran Jin, Pengfei Cao, Hongbang Yuan, Yubo Chen, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Kang Liu, and Jun Zhao. 2024 · 2024
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BadEdit: Backdooring large language models by model editing
Yanzhou Li, Tianlin Li, Kangjie Chen, Jian Zhang, Shangqing Liu, Wenhan Wang, Tianwei Zhang, and Yang Liu. 2024b · 2024
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Unveiling the pitfalls of knowledge editing for large language models
Zhoubo Li, Ningyu Zhang, Yunzhi Yao, Mengru Wang, Xi Chen, and Huajun Chen. 2024c · 2024
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Understanding LLMs: A Comprehensive Overview from Training to Inference
Yiheng Liu, Hao He, Tianle Han, Xu Zhang, Mengyuan Liu, Jiaming Tian, Yutong Zhang, Jiaqi Wang, Xiaohui Gao, Tianyang Zhong, Yi Pan, Shaochen Xu, Zihao Wu, Zhengliang Liu, Xin Zhang, Shu Zhang, Xintao Hu, Tuo Zhang, Ning Qiang, Tianming Liu, and Bao Ge. 2024 · 2024
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LLaMA: Open and Efficient Foundation Language Models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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Lionel Wong, Gabriel Grand, Alexander K. Lew, Noah D. Goodman, Vikash K. Mansinghka, Jacob Andreas, and Joshua B. Tenenbaum. 2023 · 2023
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Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond
Jingfeng Yang, Hongye Jin, Ruixiang Tang, Xiaotian Han, Qizhang Feng, Haoming Jiang, Bing Yin, and Xia Hu. 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
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The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"
Lukas Berglund, Meg Tong, Max Kaufmann, Mikita Balesni, Asa Cooper Stickland, Tomasz Korbak, and Owain Evans. 2024 · 2024
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Canyu Chen, Baixiang Huang, Zekun Li, Zhaorun Chen, Shiyang Lai, Xiongxiao Xu, Jia-Chen Gu, Jindong Gu, Huaxiu Yao, Chaowei Xiao, et al · 2024
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Leveraging Logical Rules in Knowledge Editing: A Cherry on the Top
Keyuan Cheng, Muhammad Asif Ali, Shu Yang, Gang Lin, Yuxuan Zhai, Haoyang Fei, Ke Xu, Lu Yu, Lijie Hu, and Di Wang. 2024a · 2024
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OpenAI and the Co-authors. 2024 · 2024
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Unifying Large Language Models and Knowledge Graphs: A Roadmap
Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu. 2024a · 2024
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Unifying Large Language Models and Knowledge Graphs: A Roadmap
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Long-form evaluation of model editing
Domenic Rosati, Robie Gonzales, Jinkun Chen, Xuemin Yu, Melis Erkan, Yahya Kayani, Satya Deepika Chavatapalli, Frank Rudzicz, and Hassan Sajjad. 2024 · 2024
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A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, and Aman Chadha. 2024 · 2024
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Kai Sun, Yifan Ethan Xu, Hanwen Zha, Yue Liu, and Xin Luna Dong. 2024 · 2024
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Massive Editing for Large Language Models via Meta Learning. In International Conference on Learning Representations
Chenmien Tan, Ge Zhang, and Jie Fu. 2024 · 2024
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InstructEdit: Instruction-based Knowledge Editing for Large Language Models
Bozhong Tian, Siyuan Cheng, Xiaozhuan Liang, Ningyu Zhang, Yi Hu, Kouying Xue, Yanjie Gou, Xi Chen, and Huajun Chen. 2024 · 2024
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EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models
Peng Wang, Ningyu Zhang, Bozhong Tian, Zekun Xi, Yunzhi Yao, Ziwen Xu, Mengru Wang, Shengyu Mao, Xiaohan Wang, Siyuan Cheng, Kangwei Liu, Yuansheng Ni, Guozhou Zheng, and Huajun Chen. 2024 · 2024
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Stable Knowledge Editing in Large Language Models
Zihao Wei, Liang Pang, Hanxing Ding, Jingcheng Deng, Huawei Shen, and Xueqi Cheng. 2024 · 2024
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Continual learning for large language models: A survey
Tongtong Wu, Linhao Luo, Yuan-Fang Li, Shirui Pan, Thuy-Trang Vu, and Gholamreza Haffari. 2024 · 2024
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Detecting Edited Knowledge in Language Models
Paul Youssef, Zhixue Zhao, Jörg Schlötterer, and Christin Seifert. 2024 · 2024
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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, et al · 2024
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CollabEdit: Towards Non-destructive Collaborative Knowledge Editing. In ICLR 2024 Workshop on Navigating and Addressing Data Problems for Foundation Models
Jiamu Zheng, Jinghuai Zhang, Futing Wang, Tianyu Du, and Tao Lin. [n.d.] · 2024
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