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Large language models (LLMs) have recently achieved remarkable success in various reasoning tasks in the field of natural language processing.
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 · 1901
Earlier work this paper cites.
Large language models in medicine
Arun James Thirunavukarasu, Darren Shu Jeng Ting, Kabilan Elangovan, Laura Gutierrez, Ting Fang Tan, and Daniel Shu Wei Ting. 2023 · 1940
Earlier work this paper cites.
Attention Is All You Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Chang Ming-Wei, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2020 · 2020
Earlier work this paper cites.
Learning Knowledge Graph-based World Models of Textual Environments
Prithviraj Ammanabrolu and Mark Riedl. 2021 · 2021
Earlier work this paper cites.
Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations
Jaehun Jung, Lianhui Qin, Sean Welleck, Faeze Brahman, Chandra Bhagavatula, Ronan Le Bras, and Yejin Choi. 2022 · 2022
Earlier work this paper cites.
Pure Transformers are Powerful Graph Learners
Jinwoo Kim, Dat Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, and Seunghoon Hong. 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.
Synchromesh: Reliable Code Generation from Pre-trained Language Models
Gabriel Poesia, Oleksandr Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, and Sumit Gulwani. 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
Cited alongside, same era.
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023 · 2023
Cited alongside, same era.
Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks
Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen. 2023 · 2023
Cited alongside, same era.
SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning
Keyu Duan, Qian Liu, Tat-Seng Chua, Shuicheng Yan, Wei Tsang Ooi, Qizhe Xie, and Junxian He. 2023 · 2023
Cited alongside, same era.
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V Le, et al. 2023 · 2023
Later among the works it cites.
Graph of Thoughts: Solving Elaborate Problems with Large Language Models
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, et al. 2024 · 2024
Closest in time.
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. 2024 · 2024
Closest in time.
Talk like a Graph: Encoding Graphs for Large Language Models
Bahare Fatemi, Jonathan Halcrow, and Bryan Perozzi. 2024 · 2024
Closest in time.
Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-Based Retrofitting
Xinyan Guan, Yanjiang Liu, Hongyu Lin, Yaojie Lu, Ben He, Xianpei Han, and Le Sun. 2024 · 2024
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Jiayan Guo, Lun Du, and Hengyu Liu. 2023 · 2023
Cited alongside, same era.
Beyond Text: A Deep Dive into Large Language Models’ Ability on Understanding Graph Data
Yuntong Hu, Zheng Zhang, and Liang Zhao. 2023 · 2023
Cited alongside, same era.
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023 · 2023
Cited alongside, same era.
Prompting with Pseudo-Code Instructions
Mayank Mishra, Prince Kumar, Riyaz Bhat, Rudra Murthy, Danish Contractor, and Srikanth Tamilselvam. 2023 · 2023
Cited alongside, same era.
Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs
Shengyin Sun, Yuxiang Ren, Chen Ma, and Xuecang Zhang. 2023 · 2023
Cited alongside, same era.
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. 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.
Can Language Models Solve Graph Problems in Natural Language?
Heng Wang, Shangbin Feng, Tianxing He, Zhaoxuan Tan, Xiaochuang Han, and Yulia Tsvetkov. 2023a
Cited in the paper.
GraphEdit: Large Language Models for Graph Structure Learning
Zirui Guo, Lianghao Xia, Yanhua Yu, Yuling Wang, Zixuan Yang, Wei Wei, Liang Pang, Tat-Seng Chua, and Chao Huang. 2024 · 2024
Closest in time.
Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning
Xiaoxin He, Xavier Bresson, Thomas Laurent, Adam Perold, Yann LeCun, and Bryan Hooi. 2024 · 2024
Closest in time.
Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning
Linhao Luo, Yuan-Fang Li, Gholamreza Haffari, and Shirui Pan. 2024 · 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 · 2024
Closest in time.
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.