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Large language models (LLMs) have achieved remarkable performance in natural language understanding and generation tasks.
Language models are few-shot learners
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How much knowledge can you pack into the parameters of a language model?
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The probabilistic relevance framework: Bm25 and beyond
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Improving natural language inference using external knowledge in the science questions domain
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Word2vec conjecture and a limitative result
Falcon Z. Dai. 2020 · 2020
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A survey of the state of explainable ai for natural language processing
Marina Danilevsky, Kun Qian, Ranit Aharonov, Yannis Katsis, Ban Kawas, and Prithviraj Sen. 2020 · 2020
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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 · 2020
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K-BERT: Enabling language representation with knowledge graph
Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Qi Ju, Haotang Deng, and Ping Wang. 2020 · 2020
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CoLAKE: Contextualized language and knowledge embedding
Tianxiang Sun, Yunfan Shao, Xipeng Qiu, Qipeng Guo, Yaru Hu, Xuan-Jing Huang, and Zheng Zhang. 2020 · 2020
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Complex temporal question answering on knowledge graphs
Zhen Jia, Soumajit Pramanik, Rishiraj Saha Roy, and Gerhard Weikum. 2021 · 2021
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ERNIE 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation
Yu Sun, Shuohuan Wang, Shikun Feng, Siyu Ding, Chao Pang, Junyuan Shang, Jiaxiang Liu, Xuyi Chen, Yanbin Zhao, Yuxiang Lu, et al. 2021 · 2021
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QA-GNN: Reasoning with language models and knowledge graphs for question answering
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Performance of chatgpt, gpt-4, and google bard on a neurosurgery oral boards preparation question bank
Rohaid Ali, Oliver Y Tang, Ian D Connolly, Jared S Fridley, John H Shin, Patricia L Zadnik Sullivan, Deus Cielo, Adetokunbo A Oyelese, Curtis E Doberstein, Albert E Telfeian, et al. 2022 · 2022
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PaLM: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022 · 2022
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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
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Progressive prompts: Continual learning for language models
Complex logical reasoning over knowledge graphs using large language models
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Unifying large language models and knowledge graphs: A roadmap
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Anastasia Razdaibiedina, Yuning Mao, Rui Hou, Madian Khabsa, Mike Lewis, and Amjad Almahairi. 2022 · 2022
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Deep bidirectional language-knowledge graph pretraining
Michihiro Yasunaga, Antoine Bosselut, Hongyu Ren, Xikun Zhang, Christopher D Manning, Percy S Liang, and Jure Leskovec. 2022 · 2022
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GreaseLM: Graph reasoning enhanced language models
Xikun Zhang, Antoine Bosselut, Michihiro Yasunaga, Hongyu Ren, Percy Liang, Christopher D Manning, and Jure Leskovec. 2022 · 2022
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Samy Ateia and Udo Kruschwitz. 2023 · 2023
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Knowledge-augmented language model prompting for zero-shot knowledge graph question answering
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Instruction mining: High-quality instruction data selection for large language models
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Exploring the potential of large language models (llms) in learning on graphs
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Lost in the middle: How language models use long contexts
Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2023a
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Large language models encode clinical knowledge
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Chain-of-thought prompting elicits reasoning in large language models
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