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Large language models (LLMs) have demonstrated remarkable proficiency in understanding and generating responses to complex queries through large-scale pre-training.
A (sub)graph isomorphism algorithm for matching large graphs
L.P. Cordella, P. Foggia, C. Sansone, and M. Vento. 2004 · 2004
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Dbpedia: a nucleus for a web of open data
Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. 2007 · 2007
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Facts as experts: Adaptable and interpretable neural memory over symbolic knowledge
Pat Verga, Haitian Sun, Livio Baldini Soares, and William W. Cohen. 2020 · 2007
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Freebase: a collaboratively created graph database for structuring human knowledge
Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. 2008 · 2008
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Ontology-based subgraph querying
Yinghui Wu, Shengqi Yang, and Xifeng Yan. 2013 · 2013
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Variance reduction in sgd by distributed importance sampling
Guillaume Alain, Alex Lamb, Chinnadhurai Sankar, Aaron Courville, and Yoshua Bengio. 2016 · 2016
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From freebase to wikidata: The great migration
Thomas Pellissier Tanon, Denny Vrandečić, Sebastian Schaffert, Thomas Steiner, and Lydia Pintscher. 2016 · 2016
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Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi. 2017 · 2017
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Not all samples are created equal: Deep learning with importance sampling
Angelos Katharopoulos and Francois Fleuret. 2018 · 2018
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Adafactor: Adaptive learning rates with sublinear memory cost
Noam Shazeer and Mitchell Stern. 2018 · 2018
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Open-world knowledge graph completion
Baoxu Shi and Tim Weninger. 2018 · 2018
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Learning to answer complex questions over knowledge bases with query composition
Nikita Bhutani, Xinyi Zheng, and H V Jagadish. 2019 · 2019
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COMET: Commonsense transformers for automatic knowledge graph construction
Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, and Yejin Choi. 2019 · 2019
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Commonsense knowledge mining from pretrained models
Joe Davison, Joshua Feldman, and Alexander Rush. 2019 · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
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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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Atomic: An atlas of machine commonsense for if-then reasoning
Maarten Sap, Ronan Le Bras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith, and Yejin Choi. 2019 · 2019
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Autoassist: A framework to accelerate training of deep neural networks
Jiong Zhang, Hsiang-Fu Yu, and Inderjit S Dhillon. 2019 · 2019
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The graph isomorphism problem
Martin Grohe and Pascal Schweitzer. 2020 · 2020
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Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020 · 2020
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How can we know what language models know?
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020 · 2020
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Query graph generation for answering multi-hop complex questions from knowledge bases
Yunshi Lan and Jing Jiang. 2020 · 2020
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Stepwise reasoning for multi-relation question answering over knowledge graph with weak supervision
Yunqi Qiu, Yuanzhuo Wang, Xiaolong Jin, and Kun Zhang. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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Zero: Memory optimizations toward training trillion parameter models
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He. 2020 · 2020
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. 2020 · 2020
Cited alongside, same era.
How much knowledge can you pack into the parameters of a language model?
Adam Roberts, Colin Raffel, and Noam Shazeer. 2020 · 2020
Cited alongside, same era.
Knowledge-grounded dialogue generation with a unified knowledge representation
Yu Li, Baolin Peng, Yelong Shen, Yi Mao, Lars Liden, Zhou Yu, and Jianfeng Gao. 2022c · 2022
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SKILL: Structured knowledge infusion for large language models
Fedor Moiseev, Zhe Dong, Enrique Alfonseca, and Martin Jaggi. 2022 · 2022
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Impact of pretraining term frequencies on few-shot numerical reasoning
Yasaman Razeghi, Robert L Logan IV, Matt Gardner, and Sameer Singh. 2022 · 2022
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Memorisation versus generalisation in pre-trained language models
Michael Tänzer, Sebastian Ruder, and Marek Rei. 2022 · 2022
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Rethinking knowledge graph evaluation under the open-world assumption
Haotong Yang, Zhouchen Lin, and Muhan Zhang. 2022 · 2022
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Deep bidirectional language-knowledge graph pretraining
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Improving multi-hop question answering over knowledge graphs using knowledge base embeddings
Apoorv Saxena, Aditay Tripathi, and Partha Talukdar. 2020 · 2020
Cited alongside, same era.
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
Cited alongside, same era.
Knowledgeable or educated guess? revisiting language models as knowledge bases
Boxi Cao, Hongyu Lin, Xianpei Han, Le Sun, Lingyong Yan, Meng Liao, Tong Xue, and Jin Xu. 2021 · 2021
Cited alongside, same era.
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.
Space efficient context encoding for non-task-oriented dialogue generation with graph attention transformer
Fabian Galetzka, Jewgeni Rose, David Schlangen, and Jens Lehmann. 2021 · 2021
Cited alongside, same era.
Language models as knowledge bases: On entity representations, storage capacity, and paraphrased queries
Benjamin Heinzerling and Kentaro Inui. 2021 · 2021
Cited alongside, same era.
Wikidata core for question answering
Magdalena Kaiser and Philipp Christmann. 2021 · 2021
Cited alongside, same era.
Michihiro Yasunaga, Antoine Bosselut, Hongyu Ren, Xikun Zhang, Christopher D Manning, Percy S Liang, and Jure Leskovec. 2022 · 2022
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Prompting as probing: Using language models for knowledge base construction
Dimitrios Alivanistos, Selene Báez Santamaría, Michael Cochez, Jan-Christoph Kalo, Emile van Krieken, and Thiviyan Thanapalasingam. 2023 · 2023
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Knowledge-augmented language model prompting for zero-shot knowledge graph question answering
Jinheon Baek, Alham Fikri Aji, and Amir Saffari. 2023 · 2023
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Quantifying memorization across neural language models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, and Chiyuan Zhang. 2023 · 2023
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Sok: Memorization in general-purpose large language models
Valentin Hartmann, Anshuman Suri, Vincent Bindschaedler, David Evans, Shruti Tople, and Robert West. 2023 · 2023
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Large language models struggle to learn long-tail knowledge
Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, and Colin Raffel. 2023 · 2023
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Copyright violations and large language models
Antonia Karamolegkou, Jiaang Li, Li Zhou, and Anders Søgaard. 2023 · 2023
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DLAMA: A framework for curating culturally diverse facts for probing the knowledge of pretrained language models
Amr Keleg and Walid Magdy. 2023 · 2023
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When not to trust language models: Investigating effectiveness of parametric and non-parametric memories
Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2023 · 2023
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Beyond memorization: Violating privacy via inference with large language models
Robin Staab, Mark Vero, Mislav Balunović, and Martin Vechev. 2023 · 2023
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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, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. 2023 · 2023
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Evaluating the knowledge base completion potential of GPT
Blerta Veseli, Simon Razniewski, Jan-Christoph Kalo, and Gerhard Weikum. 2023a · 2023
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Evaluating language models for knowledge base completion
Blerta Veseli, Sneha Singhania, Simon Razniewski, and Gerhard Weikum. 2023b · 2023
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Data selection for language models via importance resampling
Sang Michael Xie, Shibani Santurkar, Tengyu Ma, and Percy Liang. 2023 · 2023
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Privacy issues in large language models: A survey
Seth Neel and Peter Chang. 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, Siyuan Cheng, Ziwen Xu, Xin Xu, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Lei Liang, Zhiqiang Zhang, Xiaowei Zhu, Jun Zhou, and Huajun Chen. 2024 · 2024
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