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Given a graph with textual attributes, we enable users to `chat with their graph': that is, to ask questions about the graph using a conversational interface.
A note on the prize collecting traveling salesman problem
Daniel Bienstock, Michel X Goemans, David Simchi-Levi, and David Williamson · 1993
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A nearly-linear time framework for graph-structured sparsity
Chinmay Hegde, Piotr Indyk, and Ludwig Schmidt · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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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
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A Hudson and Christopher D Manning · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 2019
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
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Masked label prediction: Unified message passing model for semi-supervised classification
Yunsheng Shi, Zhengjie Huang, Shikun Feng, Hui Zhong, Wenjin Wang, and Yu Sun · 2020
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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Explagraphs: An explanation graph generation task for structured commonsense reasoning
Swarnadeep Saha, Prateek Yadav, Lisa Bauer, and Mohit Bansal · 2021
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
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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
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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
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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
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Talk like a graph: Encoding graphs for large language models
Bahare Fatemi, Jonathan Halcrow, and Bryan Perozzi · 2023
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Knowledge graph-augmented language models for complex question answering
Priyanka Sen, Sandeep Mavadia, and Amir Saffari · 2023
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Large language models as topological structure enhancers for text-attributed graphs
Shengyin Sun, Yuxiang Ren, Chen Ma, and Xuecang Zhang · 2023
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Graphgpt: Graph instruction tuning for large language models
Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Lixin Su, Suqi Cheng, Dawei Yin, and Chao Huang · 2023
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Graph neural prompting with large language models
Yijun Tian, Huan Song, Zichen Wang, Haozhu Wang, Ziqing Hu, Fang Wang, Nitesh V Chawla, and Panpan Xu · 2023
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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
Cited alongside, same era.
Xiaoxin He, Xavier Bresson, Thomas Laurent, Adam Perold, Yann LeCun, and Bryan Hooi · 2023
Cited alongside, same era.
Structgpt: A general framework for large language model to reason over structured data
Jinhao Jiang, Kun Zhou, Zican Dong, Keming Ye, Wayne Xin Zhao, and Ji-Rong Wen · 2023
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Large language models on graphs: A comprehensive survey
Bowen Jin, Gang Liu, Chi Han, Meng Jiang, Heng Ji, and Jiawei Han · 2023
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Knowledge graph-augmented language models for knowledge-grounded dialogue generation, 2023
Minki Kang, Jin Myung Kwak, Jinheon Baek, and Sung Ju Hwang · 2023
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Boosting logical reasoning in large language models through a new framework: The graph of thought
Bin Lei, Chunhua Liao, Caiwen Ding, et al · 2023
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Integrating graphs with large language models: Methods and prospects
Shirui Pan, Yizhen Zheng, and Yixin Liu · 2023
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Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Can language models solve graph problems in natural language?
Heng Wang, Shangbin Feng, Tianxing He, Zhaoxuan Tan, Xiaochuang Han, and Yulia Tsvetkov · 2023
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Next-gpt: Any-to-any multimodal llm
Shengqiong Wu, Hao Fei, Leigang Qu, Wei Ji, and Tat-Seng Chua · 2023
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Natural language is all a graph needs
Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu, and Yongfeng Zhang · 2023
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Multimodal graph learning for generative tasks
Minji Yoon, Jing Yu Koh, Bryan Hooi, and Ruslan Salakhutdinov · 2023
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Graph-toolformer: To empower llms with graph reasoning ability via prompt augmented by chatgpt
Jiawei Zhang · 2023
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Gimlet: A unified graph-text model for instruction-based molecule zero-shot learning
Haiteng Zhao, Shengchao Liu, Chang Ma, Hannan Xu, Jie Fu, Zhi-Hong Deng, Lingpeng Kong, and Qi Liu · 2023
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny · 2023
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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
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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 · 2024
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