Fetching the paper…
Reading the bibliography…
Large Language Models (LLMs) excel at intuitive, implicit reasoning.
Dual processes in reasoning?
Peter C Wason and J St BT Evans · 1974
Earlier work this paper cites.
The empirical case for two systems of reasoning
Steven A Sloman · 1996
Earlier work this paper cites.
Yago: a core of semantic knowledge
Fabian M Suchanek, Gjergji Kasneci, and Gerhard Weikum · 2007
Earlier work this paper cites.
Freebase: a collaboratively created graph database for structuring human knowledge
Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor · 2008
Earlier work this paper cites.
Toward an architecture for never-ending language learning
Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr Settles, Estevam Hruschka, and Tom Mitchell · 2010
Earlier work this paper cites.
Thinking, fast and slow
Daniel Kahneman · 2011
Earlier work this paper cites.
Wikidata: a free collaborative knowledgebase
Denny Vrandečić and Markus Krötzsch · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
The value of semantic parse labeling for knowledge base question answering
Wen-tau Yih, Matthew Richardson, Christopher Meek, Ming-Wei Chang, and Jina Suh · 2016
Earlier work this paper cites.
Key-value memory networks for directly reading documents
Alexander Miller, Adam Fisch, Jesse Dodge, Amir-Hossein Karimi, Antoine Bordes, and Jason Weston · 2016
Earlier work this paper cites.
Gaussian attention model and its application to knowledge base embedding and question answering
Liwen Zhang, John Winn, and Ryota Tomioka · 2016
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Earlier work this paper cites.
Open domain question answering using early fusion of knowledge bases and text
Haitian Sun, Bhuwan Dhingra, Manzil Zaheer, Kathryn Mazaitis, Ruslan Salakhutdinov, and William Cohen · 2018
Earlier work this paper cites.
Canonical tensor decomposition for knowledge base completion
Timothée Lacroix, Nicolas Usunier, and Guillaume Obozinski · 2018
Earlier work this paper cites.
The web as a knowledge-base for answering complex questions
Alon Talmor and Jonathan Berant · 2018
Earlier work this paper cites.
Variational reasoning for question answering with knowledge graph
Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander Smola, and Le Song · 2018
Earlier work this paper cites.
From system 1 deep learning to system 2 deep learning
Yoshua Bengio et al · 2019
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu · 2019
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
Improving multi-hop question answering over knowledge graphs using knowledge base embeddings
Apoorv Saxena, Aditay Tripathi, and Partha Talukdar · 2020
Cited alongside, same era.
Sparqa: skeleton-based semantic parsing for complex questions over knowledge bases
Yawei Sun, Lingling Zhang, Gong Cheng, and Yuzhong Qu · 2020
Cited alongside, same era.
Improving multi-hop knowledge base question answering by learning intermediate supervision signals
Gaole He, Yunshi Lan, Jing Jiang, Wayne Xin Zhao, and Ji-Rong Wen · 2021
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
Later among the works it cites.
Emerging drug interaction prediction enabled by a flow-based graph neural network with biomedical network
Yongqi Zhang, Quanming Yao, Ling Yue, Xian Wu, Ziheng Zhang, Zhenxi Lin, and Yefeng Zheng · 2023
Later among the works it cites.
Knowledge-augmented language model prompting for zero-shot knowledge graph question answering
Jinheon Baek, Alham Aji, and Amir Saffari · 2023
Later among the works it cites.
Can knowledge graphs reduce hallucinations in llms?: A survey
Garima Agrawal, Tharindu Kumarage, Zeyad Alghami, and Huan Liu · 2023
Later among the works it cites.
Structgpt: A general framework for large language model to reason over structured data
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
Cited alongside, same era.
Qa-gnn: Reasoning with language models and knowledge graphs for question answering
Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut, Percy Liang, and Jure Leskovec · 2021
Cited alongside, same era.
Transfernet: An effective and transparent framework for multi-hop question answering over relation graph
Jiaxin Shi, Shulin Cao, Lei Hou, Juanzi Li, and Hanwang Zhang · 2021
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Cited alongside, same era.
Knowledge graph reasoning with relational digraph
Yongqi Zhang and Quanming Yao · 2022
Cited alongside, same era.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2022
Cited alongside, same era.
Complex knowledge base question answering: A survey
Yunshi Lan, Gaole He, Jinhao Jiang, Jing Jiang, Wayne Xin Zhao, and Ji-Rong Wen · 2022
Cited alongside, same era.
Jinhao Jiang, Kun Zhou, Zican Dong, Keming Ye, Wayne Xin Zhao, and Ji-Rong Wen · 2023
Later among the works it cites.
Think-on-graph: Deep and responsible reasoning of large language model on knowledge graph
Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Lionel Ni, Heung-Yeung Shum, and Jian Guo · 2023
Later among the works it cites.
Retrieval-augmented generation for large language models: A survey
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang · 2023
Later among the works it cites.
Can retriever-augmented language models reason? the blame game between the retriever and the language model
Parishad BehnamGhader, Santiago Miret, and Siva Reddy · 2023
Later among the works it cites.
Keheng Wang, Feiyu Duan, Sirui Wang, Peiguang Li, Yunsen Xian, Chuantao Yin, Wenge Rong, and Zhang Xiong · 2023
Later among the works it cites.
Reasoning on graphs: Faithful and interpretable large language model reasoning
LINHAO LUO, Yuan-Fang Li, Reza Haf, and Shirui Pan · 2023
Later among the works it cites.
Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan · 2024
Closest in time.
Distilling system 2 into system 1
Ping Yu, Jing Xu, Jason Weston, and Ilia Kulikov · 2024
Closest in time.
Mahammed Kamruzzaman and Gene Louis Kim · 2024
Closest in time.
Unifying large language models and knowledge graphs: A roadmap
Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu · 2024
Closest in time.
Knowledge-enhanced recommendation with user-centric subgraph network
Guangyi Liu, Quanming Yao, Yongqi Zhang, and Lei Chen · 2024
Closest in time.
A survey on retrieval-augmented text generation for large language models
Yizheng Huang and Jimmy Huang · 2024
Closest in time.
Gnn-rag: Graph neural retrieval for large language model reasoning
Costas Mavromatis and George Karypis · 2024
Closest in time.