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Factual questions typically can be answered correctly at different levels of granularity.
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H Miller, and Sebastian Riedel. 2019 · 1909
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Selective-lama: Selective prediction for confidence-aware evaluation of language models
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Towards a human-like open-domain chatbot
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Ambigqa: Answering ambiguous open-domain questions
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Selective question answering under domain shift
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Wikidata: a free collaborative knowledgebase
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Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv. 2017 · 2017
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. 2017 · 2017
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Wikiqa: A challenge dataset for open-domain question answering
Yi Yang, Wen-tau Yih, and Christopher Meek. 2015 · 2018
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Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. 2019 · 2019
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Simple entity-centric questions challenge dense retrievers
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What’s in a name? answer equivalence for open-domain question answering
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It’s mbr all the way down: Modern generation techniques through the lens of minimum bayes risk
Amanda Bertsch, Alex Xie, Graham Neubig, and Matthew R Gormley. 2023 · 2023
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Adaptation with self-evaluation to improve selective prediction in llms
Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan O Arik, Tomas Pfister, and Somesh Jha. 2023 · 2023
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Markus Freitag, Behrooz Ghorbani, and Patrick Fernandes. 2023 · 2023
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The capacity for moral self-correction in large language models
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Can language models be specific? how?
Jie Huang, Kevin Chen-Chuan Chang, Jinjun Xiong, and Wen-mei Hwu. 2022 · 2022
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Language models (mostly) know what they know
Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, et al. 2022 · 2022
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Prompting gpt-3 to be reliable
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Lamda: Language models for dialog applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al. 2022 · 2022
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Self-supervised semantic-driven phoneme discovery for zero-resource speech recognition
Liming Wang, Siyuan Feng, Mark Hasegawa-Johnson, and Chang Yoo. 2022 · 2022
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Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al. 2023 · 2023
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Uncertainty-aware language modeling for selective question answering
Qi Yang, Shreya Ravikumar, Fynn Schmitt-Ulms, Satvik Lolla, Ege Demir, Iaroslav Elistratov, Alex Lavaee, Sadhana Lolla, Elaheh Ahmadi, Daniela Rus, et al. 2023a
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Ehsan Kamalloo, Nouha Dziri, Charles LA Clarke, and Davood Rafiei. 2023 · 2023
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Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar. 2023 · 2023
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When not to trust language models: Investigating effectiveness of parametric and non-parametric memories
Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2023 · 2023
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Covering uncommon ground: Gap-focused question generation for answer assessment
Roni Rabin, Alexandre Djerbetian, Roee Engelberg, Lidan Hackmon, Gal Elidan, Reut Tsarfaty, and Amir Globerson. 2023 · 2023
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Self-evaluation improves selective generation in large language models
Jie Ren, Yao Zhao, Tu Vu, Peter J Liu, and Balaji Lakshminarayanan. 2023 · 2023
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Why does chatgpt fall short in providing truthful answers
Shen Zheng, Jie Huang, and Kevin Chen-Chuan Chang. 2023 · 2023
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Selective-LAMA: Selective prediction for confidence-aware evaluation of language models
Hiyori Yoshikawa and Naoaki Okazaki. 2023a · 2028
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