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Integrating large language models (LLMs) with knowledge graphs derived from domain-specific data represents an important advancement towards more powerful and factual reasoning.
Bertscore: Evaluating text generation with bert
Zhang, T.; Kishore, V.; Wu, F.; Weinberger, K. Q.; and Artzi, Y. 2019 · 1904
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The unified medical language system (UMLS): integrating biomedical terminology
Bodenreider, O. 2004 · 2004
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Deberta: Decoding-enhanced bert with disentangled attention
He, P.; Liu, X.; Gao, J.; and Chen, W. 2020 · 2006
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ArnetMiner: Extraction and Mining of Academic Social Networks
Tang, J.; Zhang, J.; Yao, L.; Li, J.; Zhang, L.; and Su, Z. 2008 · 2008
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Shazeer, N.; Mirhoseini, A.; Maziarz, K.; Davis, A.; Le, Q.; Hinton, G.; and Dean, J. 2017 · 2017
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Lewis, P.; Perez, E.; Piktus, A.; Petroni, F.; Karpukhin, V.; Goyal, N.; Küttler, H.; Lewis, M.; Yih, W.-t.; Rocktäschel, T.; et al. 2020 · 2020
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Rasley, J.; Rajbhandari, S.; Ruwase, O.; and He, Y. 2020 · 2020
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Improving multi-hop question answering over knowledge graphs using knowledge base embeddings
Saxena, A.; Tripathi, A.; and Talukdar, P. 2020 · 2020
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Finetuned language models are zero-shot learners
Wei, J.; Bosma, M.; Zhao, V. Y.; Guu, K.; Yu, A. W.; Lester, B.; Du, N.; Dai, A. M.; and Le, Q. V. 2021 · 2021
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Graph-based Multilingual Language Model: Leveraging Product Relations for Search Relevance
Choudhary, N.; Rao, N.; Subbian, K.; and Reddy, C. K. 2022 · 2022
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Training language models to follow instructions with human feedback
Ouyang, L.; Wu, J.; Jiang, X.; Almeida, D.; Wainwright, C.; Mishkin, P.; Zhang, C.; Agarwal, S.; Slama, K.; Ray, A.; et al. 2022 · 2022
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Deep bidirectional language-knowledge graph pretraining
Yasunaga, M.; Bosselut, A.; Ren, H.; Zhang, X.; Manning, C. D.; Liang, P. S.; and Leskovec, J. 2022 · 2022
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Mixture-of-experts with expert choice routing
Zhou, Y.; Lei, T.; Liu, H.; Du, N.; Huang, Y.; Zhao, V.; Dai, A. M.; Le, Q. V.; Laudon, J.; et al. 2022 · 2022
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The Reversal Curse: LLMs trained on” A is B” fail to learn” B is A”
Berglund, L.; Tong, M.; Kaufmann, M.; Balesni, M.; Stickland, A. C.; Korbak, T.; and Evans, O. 2023 · 2023
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Towards graph foundation models: A survey and beyond
Liu, J.; Yang, C.; Lu, Z.; Chen, J.; Li, Y.; Zhang, M.; Bai, T.; Fang, Y.; Sun, L.; Yu, P. S.; et al. 2023 · 2023
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Capabilities of gpt-4 on medical challenge problems
Nori, H.; King, N.; McKinney, S. M.; Carignan, D.; and Horvitz, E. 2023 · 2023
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Unifying Large Language Models and Knowledge Graphs: A Roadmap
Pan, S.; Luo, L.; Wang, Y.; Chen, C.; Wang, J.; and Wu, X. 2023 · 2023
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Sprueill, H. W.; Edwards, C.; Olarte, M. V.; Sanyal, U.; Ji, H.; and Choudhury, S. 2023 · 2023
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Talk like a graph: Encoding graphs for large language models
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Hao, S.; Gu, Y.; Ma, H.; Hong, J. J.; Wang, Z.; Wang, D. Z.; and Hu, Z. 2023 · 2023
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Structgpt: A general framework for large language model to reason over structured data
Jiang, J.; Zhou, K.; Dong, Z.; Ye, K.; Zhao, W. X.; and Wen, J.-R. 2023 · 2023
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Large Language Models on Graphs: A Comprehensive Survey
Jin, B.; Liu, G.; Han, C.; Jiang, M.; Ji, H.; and Han, J. 2023 · 2023
Cited alongside, same era.
Sun, J.; Xu, C.; Tang, L.; Wang, S.; Lin, C.; Gong, Y.; Shum, H.-Y.; and Guo, J. 2023 · 2023
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Graph neural prompting with large language models
Tian, Y.; Song, H.; Wang, Z.; Wang, H.; Hu, Z.; Wang, F.; Chawla, N. V.; and Xu, P. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Touvron, H.; Martin, L.; Stone, K.; Albert, P.; Almahairi, A.; Babaei, Y.; Bashlykov, N.; Batra, S.; Bhargava, P.; Bhosale, S.; et al. 2023 · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Yao, S.; Yu, D.; Zhao, J.; Shafran, I.; Griffiths, T. L.; Cao, Y.; and Narasimhan, K. 2023 · 2023
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