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Biomedical reasoning integrates structured, codified knowledge with tacit, experience-driven insights.
The unified medical language system (umls): integrating biomedical terminology
Olivier Bodenreider · 2004
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Thinking and reasoning in medicine
Vimla L Patel, José F Arocha, and Jiajie Zhang · 2005
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True knowledge: Open-domain question answering using structured knowledge and inference
William Tunstall-Pedoe · 2010
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Prominent streak discovery in sequence data
Xiao Jiang, Chengkai Li, Ping Luo, Min Wang, and Yong Yu · 2011
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T-verifier: Verifying truthfulness of fact statements
Xian Li, Weiyi Meng, and Clement Yu · 2011
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The fact-checking universe in spring 2012
Lucas Graves and Tom Glaisyer · 2012
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On” one of the few” objects
You Wu, Pankaj K Agarwal, Chengkai Li, Jun Yang, and Cong Yu · 2012
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Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko · 2013
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Data in, fact out: Automated monitoring of facts by factwatcher
Naeemul Hassan, Afroza Sultana, You Wu, Gensheng Zhang, Chengkai Li, Jun Yang, and Cong Yu · 2014
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Computational fact checking from knowledge networks
Giovanni Luca Ciampaglia, Prashant Shiralkar, Luis M Rocha, Johan Bollen, Filippo Menczer, and Alessandro Flammini · 2015
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Uncovering disease-disease relationships through the incomplete interactome
Jörg Menche, Amitabh Sharma, Maksim Kitsak, Susan Dina Ghiassian, Marc Vidal, Joseph Loscalzo, and Albert-László Barabási · 2015
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Discriminative predicate path mining for fact checking in knowledge graphs
Baoxu Shi and Tim Weninger · 2016
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Attention is all you need
A Vaswani · 2017
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Improving language understanding by generative pre-training
Alec Radford · 2018
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih · 2020
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Webgpt: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al · 2021
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QA-GNN: Reasoning with language models and knowledge graphs for question answering
Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut, Percy Liang, and Jure Leskovec · 2021
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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 · 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 Gray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe · 2022
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Jointlk: Joint reasoning with language models and knowledge graphs for commonsense question answering
Yueqing Sun, Qi Shi, Le Qi, and Yu Zhang · 2022
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Biomedlm: a domain-specific large language model for biomedical text
A Venigalla, Jonathan Frankle, and M Carbin · 2022
Cited alongside, same era.
Deep bidirectional language-knowledge graph pretraining
Michihiro Yasunaga, Antoine Bosselut, Hongyu Ren, Xikun Zhang, Christopher D. Manning, Percy Liang, and Jure Leskovec · 2022
Cited alongside, same era.
Autonomous chemical research with large language models
Daniil A Boiko, Robert MacKnight, Ben Kline, and Gabe Gomes · 2023
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Augmenting large language models with chemistry tools
Andres M Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew White, and Philippe Schwaller · 2023
Cited alongside, same era.
Building a knowledge graph to enable precision medicine
Payal Chandak, Kexin Huang, and Marinka Zitnik · 2023
Cited alongside, same era.
Medalpaca–an open-source collection of medical conversational ai models and training data
Large language models are not robust multiple choice selectors
Chujie Zheng, Hao Zhou, Fandong Meng, Jie Zhou, and Minlie Huang · 2023
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Evaluating correctness and faithfulness of instruction-following models for question answering
Vaibhav Adlakha, Parishad BehnamGhader, Xing Han Lu, Nicholas Meade, and Siva Reddy · 2024
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Openbiollms: Advancing open-source large language models for healthcare and life sciences
Malaikannan Sankarasubbu Ankit Pal · 2024
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Self-RAG: Learning to retrieve, generate, and critique through self-reflection
Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi · 2024
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Bias and unfairness in information retrieval systems: New challenges in the llm era
Sunhao Dai, Chen Xu, Shicheng Xu, Liang Pang, Zhenhua Dong, and Jun Xu · 2024
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Tianyu Han, Lisa C Adams, Jens-Michalis Papaioannou, Paul Grundmann, Tom Oberhauser, Alexander Löser, Daniel Truhn, and Keno K Bressem · 2023
Cited alongside, same era.
Large language models answer medical questions accurately, but can’t match clinicians’ knowledge
Emily Harris · 2023
Cited alongside, same era.
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed · 2023
Cited alongside, same era.
Camel: Communicative agents for” mind” exploration of large language model society
Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem · 2023
Cited alongside, same era.
Towards electronic health record-based medical knowledge graph construction, completion, and applications: A literature study
Lino Murali, G Gopakumar, Daleesha M Viswanathan, and Prema Nedungadi · 2023
Cited alongside, same era.
Large language models sensitivity to the order of options in multiple-choice questions
Pouya Pezeshkpour and Estevam Hruschka · 2023
Cited alongside, same era.
Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessi, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2023
Cited alongside, same era.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, and Angela Fan · 2024
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From local to global: A graph rag approach to query-focused summarization
Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, and Jonathan Larson · 2024
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A survey on rag meeting llms: Towards retrieval-augmented large language models
Wenqi Fan, Yujuan Ding, Liangbo Ning, Shijie Wang, Hengyun Li, Dawei Yin, Tat-Seng Chua, and Qing Li · 2024
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Empowering biomedical discovery with ai agents
Shanghua Gao, Ada Fang, Yepeng Huang, Valentina Giunchiglia, Ayush Noori, Jonathan Richard Schwarz, Yasha Ektefaie, Jovana Kondic, and Marinka Zitnik · 2024
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Few shot chain-of-thought driven reasoning to prompt llms for open ended medical question answering
Ojas Gramopadhye, Saeel Sandeep Nachane, Prateek Chanda, Ganesh Ramakrishnan, Kshitij Sharad Jadhav, Yatin Nandwani, Dinesh Raghu, and Sachindra Joshi · 2024
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Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, and Jong C Park · 2024
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Can multiple-choice questions really be useful in detecting the abilities of llms?
Wangyue Li, Liangzhi Li, Tong Xiang, Xiao Liu, Wei Deng, and Noa Garcia · 2024
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Can large language models reason about medical questions?
Valentin Liévin, Christoffer Egeberg Hother, Andreas Geert Motzfeldt, and Ole Winther · 2024
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Afrimed-qa: A pan-african, multi-specialty, medical question-answering benchmark dataset
Tobi Olatunji, Charles Nimo, Abraham Owodunni, Tassallah Abdullahi, Emmanuel Ayodele, Mardhiyah Sanni, Chinemelu Aka, Folafunmi Omofoye, Foutse Yuehgoh, Timothy Faniran, et al · 2024
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OpenAI · 2024
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Learning from both structural and textual knowledge for inductive knowledge graph completion
Kunxun Qi, Jianfeng Du, and Hai Wan · 2024
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Generate-then-ground in retrieval-augmented generation for multi-hop question answering
Zhengliang Shi, Shuo Zhang, Weiwei Sun, Shen Gao, Pengjie Ren, Zhumin Chen, and Zhaochun Ren · 2024
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou · 2024
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Benchmarking retrieval-augmented generation for medicine
Guangzhi Xiong, Qiao Jin, Zhiyong Lu, and Aidong Zhang · 2024
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KG-rank: Enhancing large language models for medical QA with knowledge graphs and ranking techniques
Rui Yang, Haoran Liu, Edison Marrese-Taylor, Qingcheng Zeng, Yuhe Ke, Wanxin Li, Lechao Cheng, Qingyu Chen, James Caverlee, Yutaka Matsuo, and Irene Li · 2024
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End-to-end beam retrieval for multi-hop question answering
Jiahao Zhang, Haiyang Zhang, Dongmei Zhang, Liu Yong, and Shen Huang · 2024
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