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We seek to address a core challenge facing current Large Language Models (LLMs).
What can neural networks reason about?
Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2019 · 1905
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Algorithm Design
Jon Kleinberg and Eva Tardos. 2005 · 2005
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. 2015 · 2015
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Neural programmer-interpreters
Scott Reed and Nando De Freitas. 2015 · 2015
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Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015 · 2015
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Neural combinatorial optimization with reinforcement learning
Irwan Bello, Hieu Pham, Quoc V Le, Mohammad Norouzi, and Samy Bengio. 2016 · 2016
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Hybrid computing using a neural network with dynamic external memory
Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwińska, Sergio Gómez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John Agapiou, et al. 2016 · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
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Geometric deep learning: going beyond euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. 2017 · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017 · 2017
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Representation learning on graphs: Methods and applications
William L Hamilton, Rex Ying, and Jure Leskovec. 2017 · 2017
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Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al. 2018 · 2018
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Attention, learn to solve routing problems!
Wouter Kool, Herke van Hoof, and Max Welling. 2018 · 2018
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Learning a sat solver from single-bit supervision
Daniel Selsam, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura, and David L Dill. 2018 · 2018
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Inference in probabilistic graphical models by graph neural networks
KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard Zemel, and Xaq Pitkow. 2018 · 2018
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Guiding high-performance sat solvers with unsat-core predictions
Daniel Selsam and Nikolaj Bjørner. 2019 · 2019
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Neural algorithmic reasoners are implicit planners
Andreea Deac, Petar Veličković, Ognjen Milinković, Pierre-Luc Bacon, Jian Tang, and Mladen Nikolić. 2021 · 2021
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Evaluating large language models on graphs: Performance insights and comparative analysis
Chang Liu and Bo Wu. 2023 · 2023
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Danilo Numeroso, Davide Bacciu, and Petar Veličković. 2023 · 2023
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OpenAI. 2023 · 2023
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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, et al. 2023 · 2023
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Can language models solve graph problems in natural language?
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Petar Velickovic and Charles Blundell. 2021 · 2021
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How to transfer algorithmic reasoning knowledge to learn new algorithms?
Louis-Pascal Xhonneux, Andreea-Ioana Deac, Petar Veličković, and Jian Tang. 2021 · 2021
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A generalist neural algorithmic learner
Borja Ibarz, Vitaly Kurin, George Papamakarios, Kyriacos Nikiforou, Mehdi Bennani, Róbert Csordás, Andrew Dudzik, Matko Bošnjak, Alex Vitvitskyi, Yulia Rubanova, Andreea Deac, Beatrice Bevilacqua, Yaroslav Ganin, Charles Blundell, and Petar Veličković. 2022 · 2022
Cited alongside, same era.
The CLRS algorithmic reasoning benchmark
Petar Veličković, Adrià Puigdomènech Badia, David Budden, Razvan Pascanu, Andrea Banino, Misha Dashevskiy, Raia Hadsell, and Charles Blundell. 2022 · 2022
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Neural algorithmic reasoning with causal regularisation
Beatrice Bevilacqua, Kyriacos Nikiforou, Borja Ibarz, Ioana Bica, Michela Paganini, Charles Blundell, Jovana Mitrovic, and Petar Veličković. 2023 · 2023
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Exploring the potential of large language models (llms) in learning on graphs
Zhikai Chen, Haitao Mao, Hang Li, Wei Jin, Hongzhi Wen, Xiaochi Wei, Shuaiqiang Wang, Dawei Yin, Wenqi Fan, Hui Liu, et al. 2023 · 2023
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Specializing smaller language models towards multi-step reasoning
Yao Fu, Hao Peng, Litu Ou, Ashish Sabharwal, and Tushar Khot. 2023 · 2023
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Jiayan Guo, Lun Du, and Hengyu Liu. 2023 · 2023
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Heng Wang, Shangbin Feng, Tianxing He, Zhaoxuan Tan, Xiaochuang Han, and Yulia Tsvetkov. 2023 · 2023
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Llm4dyg: Can large language models solve problems on dynamic graphs?
Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Yijian Qin, Simin Wu, and Wenwu Zhu. 2023 · 2023
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Graphtext: Graph reasoning in text space
Jianan Zhao, Le Zhuo, Yikang Shen, Meng Qu, Kai Liu, Michael Bronstein, Zhaocheng Zhu, and Jian Tang. 2023 · 2023
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Exploring the Limitations of Graph Reasoning in Large Language Models
Palaash Agrawal, Shavak Vasania, and Cheston Tan. 2024 · 2024
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Topologies of Reasoning: Demystifying Chains, Trees, and Graphs of Thoughts
Maciej Besta, Florim Memedi, Zhenyu Zhang, Robert Gerstenberger, Nils Blach, Piotr Nyczyk, Marcin Copik, Grzegorz Kwaśniewski, Jürgen Müller, Lukas Gianinazzi, Ales Kubicek, Hubert Niewiadomski, Onur Mutlu, and Torsten Hoefler. 2024 · 2024
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Graph Descriptive Order Improves Reasoning with Large Language Model
Yuyao Ge, Shenghua Liu, Wenjie Feng, Lingrui Mei, Lizhe Chen, and Xueqi Cheng. 2024 · 2024
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A Survey of Graph Meets Large Language Model: Progress and Future Directions
Yuhan Li, Zhixun Li, Peisong Wang, Jia Li, Xiangguo Sun, Hong Cheng, and Jeffrey Xu Yu. 2024 · 2024
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The clrs-text algorithmic reasoning language benchmark
Larisa Markeeva, Sean McLeish, Borja Ibarz, Wilfried Bounsi, Olga Kozlova, Alex Vitvitskyi, Charles Blundell, Tom Goldstein, Avi Schwarzschild, and Petar Veličković. 2024 · 2024
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Benchmarking chatgpt on algorithmic reasoning
Sean McLeish, Avi Schwarzschild, and Tom Goldstein. 2024 · 2024
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