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Large Language Models (LLMs) are widely used for knowledge-seeking yet suffer from hallucinations.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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Are we modeling the task or the annotator? an investigation of annotator bias in natural language understanding datasets
Mor Geva, Yoav Goldberg, and Jonathan Berant · 2019
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Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher · 2019
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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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 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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Generalization through memorization: Nearest neighbor language models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis · 2020
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’just because you are right, doesn’t mean i am wrong’: Overcoming a bottleneck in the development and evaluation of open-ended visual question answering (vqa) tasks
Man Luo;Shailaja Keyur Sampat;Riley Tallman;Yankai Zeng;Manuha Vancha;Akarshan Sajja;Chitta Baral · 2021
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Zero-shot visual question answering using knowledge graph
Zhuo Chen;Jiaoyan Chen;Yuxia Geng;Jeff Z. Pan;Zonggang Yuan & Huajun Chen · 2021
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Next-qa: Next phase of question-answering to explaining temporal actions
Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua · 2021
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Language models (mostly) know what they know, 2022
Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, Scott Johnston, Sheer El-Showk, Andy Jones, Nelson Elhage, Tristan Hume, Anna Chen, Yuntao Bai, Sam Bowman, Stanislav Fort, Deep Ganguli, Danny Hernandez, Josh Jacobson, Jackson Kernion, Shauna Kravec, Liane Lovitt, Kamal Ndousse, Catherine Olsson, Sam Ringer, Dario Amodei, Tom Brown, Jack Clark, Nicholas Joseph, Ben Mann, Sam McCandlish, Chris Olah, and Jared Kaplan · 2022
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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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Webgpt: Browser-assisted question-answering with human feedback, 2022
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, and John Schulman · 2022
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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 Chi, Quoc V Le, and Denny Zhou · 2022
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Knowledge of knowledge: Exploring known-unknowns uncertainty with large language models
Alfonso Amayuelas, Liangming Pan, Wenhu Chen, and William Wang · 2023
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Towards answering open-ended ethical quandary questions, 2023
Yejin Bang, Nayeon Lee, Tiezheng Yu, Leila Khalatbari, Yan Xu, Samuel Cahyawijaya, Dan Su, Bryan Wilie, Romain Barraud, Elham J. Barezi, Andrea Madotto, Hayden Kee, and Pascale Fung · 2023
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Lang Cao · 2023
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Multi-CLS BERT: An efficient alternative to traditional ensembling
Haw-Shiuan Chang, Ruei-Yao Sun, Kathryn Ricci, and Andrew McCallum · 2023
Cited alongside, same era.
Adaptation with self-evaluation to improve selective prediction in LLMs
Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan Arik, Tomas Pfister, and Somesh Jha · 2023
Automatic evaluation of attribution by large language models
Xiang Yue, Boshi Wang, Ziru Chen, Kai Zhang, Yu Su, and Huan Sun · 2023
Later among the works it cites.
Alleviating hallucinations of large language models through induced hallucinations
Yue Zhang, Leyang Cui, Wei Bi, and Shuming Shi · 2023
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Knowing what llms do not know: A simple yet effective self-detection method
Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Chong Meng, Shuaiqiang Wang, Zhicong Cheng, Zhaochun Ren, and Dawei Yin · 2023
Later among the works it cites.
Can knowledge graphs reduce hallucinations in llms? : A survey, 2024
Garima Agrawal, Tharindu Kumarage, Zeyad Alghamdi, and Huan Liu · 2024
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Graph of Thoughts: Solving Elaborate Problems with Large Language Models
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michał Podstawski, Hubert Niewiadomski, Piotr Nyczyk, and Torsten Hoefler · 2024
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Cited alongside, same era.
Investigating the factual knowledge boundary of large language models with retrieval augmentation, 2023
Ruiyang Ren, Yuhao Wang, Yingqi Qu, Wayne Xin Zhao, Jing Liu, Hao Tian, Hua Wu, Ji-Rong Wen, and Haifeng Wang · 2023
Cited alongside, same era.
Leveraging large language models for multiple choice question answering, 2023
Joshua Robinson, Christopher Michael Rytting, and David Wingate · 2023
Cited alongside, same era.
Prompting GPT-3 to be reliable
Chenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang, Jordan Lee Boyd-Graber, and Lijuan Wang · 2023
Cited alongside, same era.
Self-knowledge guided retrieval augmentation for large language models
Yile Wang, Peng Li, Maosong Sun, and Yang Liu · 2023
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models, 2023
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou · 2023
Cited alongside, same era.
Yuqing Yang, Ethan Chern, Xipeng Qiu, Graham Neubig, and Pengfei Liu · 2023
Cited alongside, same era.
Tree of Thoughts: Deliberate problem solving with large language models, 2023
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
Cited alongside, same era.
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Principled instructions are all you need for questioning llama-1/2, gpt-3.5/4, 2024
Sondos Mahmoud Bsharat, Aidar Myrzakhan, and Zhiqiang Shen · 2024
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Yang Deng, Yong Zhao, Moxin Li, See-Kiong Ng, and Tat-Seng Chua · 2024
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Towards understanding factual knowledge of large language models
Xuming Hu, Junzhe Chen, Xiaochuan Li, Yufei Guo, Lijie Wen, Philip S. Yu, and Zhijiang Guo · 2024
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Injecting new knowledge into large language models via supervised fine-tuning, 2024
Nick Mecklenburg, Yiyou Lin, Xiaoxiao Li, Daniel Holstein, Leonardo Nunes, Sara Malvar, Bruno Silva, Ranveer Chandra, Vijay Aski, Pavan Kumar Reddy Yannam, Tolga Aktas, and Todd Hendry · 2024
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Shiyu Ni, Keping Bi, Jiafeng Guo, and Xueqi Cheng · 2024
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Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts, 2024
Jian Xie, Kai Zhang, Jiangjie Chen, Renze Lou, and Yu Su · 2024
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Rejection improves reliability: Training llms to refuse unknown questions using rl from knowledge feedback, 2024
Hongshen Xu, Zichen Zhu, Situo Zhang, Da Ma, Shuai Fan, Lu Chen, and Kai Yu · 2024
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Xunjian Yin, Xu Zhang, Jie Ruan, and Xiaojun Wan · 2024
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