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Large Language Models (LLMs) have achieved great success in various reasoning tasks.
What does bert learn about the structure of language?
Ganesh Jawahar, Benoît Sagot, and Djamé Seddah. 2019 · 2019
Earlier work this paper cites.
End-to-end structure-aware convolutional networks for knowledge base completion
Chao Shang, Yun Tang, Jing Huang, Jinbo Bi, Xiaodong He, and Bowen Zhou. 2019 · 2019
Earlier work this paper cites.
Long range arena: A benchmark for efficient transformers
Yi Tay, Mostafa Dehghani, Samira Abnar, Yikang Shen, Dara Bahri, Philip Pham, Jinfeng Rao, Liu Yang, Sebastian Ruder, and Donald Metzler. 2020 · 2020
Earlier work this paper cites.
Selection-inference: Exploiting large language models for interpretable logical reasoning
Antonia Creswell, Murray Shanahan, and Irina Higgins. 2022 · 2022
Earlier work this paper cites.
Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch. 2022 · 2022
Earlier work this paper cites.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Earlier work this paper cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
Earlier work this paper cites.
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 · 2022
Earlier work this paper cites.
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. 2023 · 2023
Earlier work this paper cites.
Autonomous chemical research with large language models
Daniil A Boiko, Robert MacKnight, Ben Kline, and Gabe Gomes. 2023 · 2023
Earlier work this paper cites.
Graphllm: Boosting graph reasoning ability of large language model
Ziwei Chai, Tianjie Zhang, Liang Wu, Kaiqiao Han, Xiaohai Hu, Xuanwen Huang, and Yang Yang. 2023 · 2023
Cited alongside, same era.
Selection-inference: Exploiting large language models for interpretable logical reasoning
Antonia Creswell, Murray Shanahan, and Irina Higgins. 2023 · 2023
Cited alongside, same era.
Talk like a graph: Encoding graphs for large language models
Bahare Fatemi, Jonathan Halcrow, and Bryan Perozzi. 2023 · 2023
Cited alongside, same era.
Looped transformers as programmable computers
Angeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D Lee, and Dimitris Papailiopoulos. 2023 · 2023
Cited alongside, same era.
Graphwiz: An instruction-following language model for graph problems
Nuo Chen, Yuhan Li, Jianheng Tang, and Jia Li. 2024 · 2024
Closest in time.
Simulation of graph algorithms with looped transformers
Artur Back de Luca and Kimon Fountoulakis. 2024 · 2024
Closest in time.
Towards revealing the mystery behind chain of thought: a theoretical perspective
Guhao Feng, Bohang Zhang, Yuntian Gu, Haotian Ye, Di He, and Liwei Wang. 2024 · 2024
Closest in time.
Visiongraph: Leveraging large multimodal models for graph theory problems in visual context
Yunxin Li, Baotian Hu, Haoyuan Shi, Wei Wang, Longyue Wang, and Min Zhang. 2024 · 2024
Closest in time.
Toolnet: Connecting large language models with massive tools via tool graph
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Jiayan Guo, Lun Du, Hengyu Liu, Mengyu Zhou, Xinyi He, and Shi Han. 2023 · 2023
Cited alongside, same era.
Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023 · 2023
Cited alongside, same era.
Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct
Haipeng Luo, Qingfeng Sun, Can Xu, Pu Zhao, Jianguang Lou, Chongyang Tao, Xiubo Geng, Qingwei Lin, Shifeng Chen, and Dongmei Zhang. 2023 · 2023
Cited alongside, same era.
Use chat gpt to solve programming bugs
Nigar M Shafiq Surameery and Mohammed Y Shakor. 2023 · 2023
Cited alongside, same era.
Looped transformers are better at learning learning algorithms
Liu Yang, Kangwook Lee, Robert Nowak, and Dimitris Papailiopoulos. 2023 · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Understanding transformer reasoning capabilities via graph algorithms
Clayton Sanford, Bahare Fatemi, Ethan Hall, Anton Tsitsulin, Mehran Kazemi, Jonathan Halcrow, Bryan Perozzi, and Vahab Mirrokni. 2024a
Cited in the paper.
Transformers, parallel computation, and logarithmic depth
Clayton Sanford, Daniel Hsu, and Matus Telgarsky. 2024b
Cited in the paper.
Xukun Liu, Zhiyuan Peng, Xiaoyuan Yi, Xing Xie, Lirong Xiang, Yuchen Liu, and Dongkuan Xu. 2024 · 2024
Closest in time.
Graphinstruct: Empowering large language models with graph understanding and reasoning capability
Zihan Luo, Xiran Song, Hong Huang, Jianxun Lian, Chenhao Zhang, Jinqi Jiang, Xing Xie, and Hai Jin. 2024 · 2024
Closest in time.
Benchmarking chatgpt on algorithmic reasoning
Sean McLeish, Avi Schwarzschild, and Tom Goldstein. 2024 · 2024
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Let your graph do the talking: Encoding structured data for llms
Bryan Perozzi, Bahare Fatemi, Dustin Zelle, Anton Tsitsulin, Mehran Kazemi, Rami Al-Rfou, and Jonathan Halcrow. 2024 · 2024
Closest in time.
Can graph learning improve task planning?
Xixi Wu, Yifei Shen, Caihua Shan, Kaitao Song, Siwei Wang, Bohang Zhang, Jiarui Feng, Hong Cheng, Wei Chen, Yun Xiong, et al. 2024 · 2024
Closest in time.