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Large language models (LLMs) have been widely studied for their ability to store and utilize positive knowledge.
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 · 1901
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Natural language generation challenges for explainable ai
Ehud Reiter. 2019 · 1911
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The role of negation in human knowledge
Charles MacDonald. 1965 · 1965
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Negative expertise
Marvin Minsky. 1997 · 1997
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Truthmakers for negative truths
George Molnar. 2000 · 2000
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Open mind common sense: Knowledge acquisition from the general public
Push Singh, Thomas Lin, Erik T. Mueller, Grace Lim, Travell Perkins, and Wan Li Zhu. 2002 · 2002
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Intensional logic
Melvin Fitting. 2006 · 2006
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Dbpedia: A nucleus for a web of open data
Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. 2007 · 2007
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A brief history of negation
J.L. Speranza and Laurence R. Horn. 2010 · 2010
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Being positive about negative facts
Stephen Barker and Mark Jago. 2012 · 2012
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Wikidata: a free collaborative knowledgebase
Denny Vrandečić and Markus Krötzsch. 2014 · 2014
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Earlier work this paper cites.
Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi. 2017 · 2017
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e-snli: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018
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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Earlier work this paper cites.
CommonsenseQA: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
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Ancient Logic
Susanne Bobzien. 2020 · 2020
Earlier work this paper cites.
Transformers as soft reasoners over language
Peter Clark, Oyvind Tafjord, and Kyle Richardson. 2020 · 2020
Earlier work this paper cites.
What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models
Allyson Ettinger. 2020 · 2020
Cited alongside, same era.
Negated and misprimed probes for pretrained language models: Birds can talk, but cannot fly
Nora Kassner and Hinrich Schütze. 2020 · 2020
Cited alongside, same era.
CommonGen: A constrained text generation challenge for generative commonsense reasoning
Bill Yuchen Lin, Wangchunshu Zhou, Ming Shen, Pei Zhou, Chandra Bhagavatula, Yejin Choi, and Xiang Ren. 2020 · 2020
Cited alongside, same era.
How context affects language models’ factual predictions
Fabio Petroni, Patrick Lewis, Aleksandra Piktus, Tim Rocktäschel, Yuxiang Wu, Alexander H. Miller, and Sebastian Riedel. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
Cited alongside, same era.
E-KAR: A benchmark for rationalizing natural language analogical reasoning
Jiangjie Chen, Rui Xu, Ziquan Fu, Wei Shi, Zhongqiao Li, Xinbo Zhang, Changzhi Sun, Lei Li, Yanghua Xiao, and Hao Zhou. 2022 · 2022
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Unsupervised explanation generation via correct instantiations
Sijie Cheng, Zhiyong Wu, Jiangjie Chen, Zhixing Li, Yang Liu, and Lingpeng Kong. 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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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022 · 2022
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Neural text generation with unlikelihood training
Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, and Jason Weston. 2020 · 2020
Cited alongside, same era.
Explanations for CommonsenseQA: New Dataset and Models
Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, Dinesh Khandelwal, Parag Singla, and Dinesh Garg. 2021 · 2021
Cited alongside, same era.
Negative knowledge for open-world wikidata
Hiba Arnaout, Simon Razniewski, Gerhard Weikum, and Jeff Z. Pan. 2021 · 2021
Cited alongside, same era.
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. 2021 · 2021
Cited alongside, same era.
Shortcutted commonsense: Data spuriousness in deep learning of commonsense reasoning
Ruben Branco, António Branco, João António Rodrigues, and João Ricardo Silva. 2021 · 2021
Cited alongside, same era.
Knowledgeable or educated guess? revisiting language models as knowledge bases
Boxi Cao, Hongyu Lin, Xianpei Han, Le Sun, Lingyong Yan, Meng Liao, Tong Xue, and Jin Xu. 2021 · 2021
Cited alongside, same era.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. 2021 · 2021
Cited alongside, same era.
Reto Gubelmann and Siegfried Handschuh. 2022 · 2022
Later among the works it cites.
Negation
Laurence R. Horn and Heinrich Wansing. 2022 · 2022
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An analysis of negation in natural language understanding corpora
Md Mosharaf Hossain, Dhivya Chinnappa, and Eduardo Blanco. 2022 · 2022
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Can large language models truly understand prompts? a case study with negated prompts
Joel Jang, Seonghyeon Ye, and Minjoon Seo. 2022 · 2022
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Maieutic prompting: Logically consistent reasoning with recursive explanations
Jaehun Jung, Lianhui Qin, Sean Welleck, Faeze Brahman, Chandra Bhagavatula, Ronan Le Bras, and Yejin Choi. 2022 · 2022
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MetaICL: Learning to learn in context
Sewon Min, Mike Lewis, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2022a · 2022
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Chatgpt
OpenAI. 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 Gray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
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Condaqa: A contrastive reading comprehension dataset for reasoning about negation
Abhilasha Ravichander, Matt Gardner, and Ana Marasović. 2022 · 2022
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Breakpoint transformers for modeling and tracking intermediate beliefs
Kyle Richardson, Ronen Tamari, Oren Sultan, Reut Tsarfaty, Dafna Shahaf, and Ashish Sabharwal. 2022 · 2022
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Learning to retrieve prompts for in-context learning
Ohad Rubin, Jonathan Herzig, and Jonathan Berant. 2022 · 2022
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Bloom: A 176b-parameter open-access multilingual language model
Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al. 2022 · 2022
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Entailer: Answering questions with faithful and truthful chains of reasoning
Oyvind Tafjord, Bhavana Dalvi Mishra, and Peter Clark. 2022 · 2022
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Debiasing nlu models via causal intervention and counterfactual reasoning
Bing Tian, Yixin Cao, Yong Zhang, and Chunxiao Xing. 2022 · 2022
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, and Denny Zhou. 2022 · 2022
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A survey of knowledge-enhanced text generation
Wenhao Yu, Chenguang Zhu, Zaitang Li, Zhiting Hu, Qingyun Wang, Heng Ji, and Meng Jiang. 2022 · 2022
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Are rotten apples edible? challenging commonsense inference ability with exceptions
Nam Do and Ellie Pavlick. 2021 · 2073
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