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Large Language Models (LLMs) have demonstrated impressive inferential capabilities, with numerous research endeavors devoted to enhancing this capacity through prompting.
Critique of pure reason
Immanuel Kant, John Miller Dow Meiklejohn, Thomas Kingsmill Abbott, and James Creed Meredith · 1934
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Empiricism and the philosophy of mind
Wilfrid Sellars et al · 1956
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Principles of artificial intelligence
Nils J Nilsson · 1982
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Kant’s transcendental idealism
Henry E Allison · 2004
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Rationalism vs. empiricism
Peter Markie and Marina Folescu · 2004
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Artificial intelligence: structures and strategies for complex problem solving
George F Luger · 2005
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Georg Wilhelm Friedrich Hegel: the science of logic
Georg Wilhelm Fredrich Hegel · 2010
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Thinking, fast and slow
Daniel Kahneman · 2011
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The revolutionary Kant: A commentary on the critique of pure reason
Graham Bird · 2013
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History of western philosophy: Collectors edition
Anthony Gottlieb and Bertrand Russell · 2013
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Earlier work this paper cites.
Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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The bounds of sense: An essay on Kant’s critique of pure reason
Peter Strawson and Lucy Allais · 2018
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant · 2019
Earlier work this paper cites.
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, et al · 2020
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Unsupervised commonsense question answering with self-talk, 2020
Vered Shwartz, Peter West, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi · 2020
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Training verifiers to solve math word problems, 2021
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman · 2021
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Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies
Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant · 2021
Earlier work this paper cites.
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing, 2021
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2021
Earlier work this paper cites.
Prompting contrastive explanations for commonsense reasoning tasks, 2021
Bhargavi Paranjape, Julian Michael, Marjan Ghazvininejad, Luke Zettlemoyer, and Hannaneh Hajishirzi · 2021
Earlier work this paper cites.
Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks, 2022
Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W. Cohen · 2022
Earlier work this paper cites.
Selection-inference: Exploiting large language models for interpretable logical reasoning, 2022
Antonia Creswell, Murray Shanahan, and Irina Higgins · 2022
Cited alongside, same era.
Language model cascades, 2022
David Dohan, Winnie Xu, Aitor Lewkowycz, Jacob Austin, David Bieber, Raphael Gontijo Lopes, Yuhuai Wu, Henryk Michalewski, Rif A. Saurous, Jascha Sohl-dickstein, Kevin Murphy, and Charles Sutton · 2022
Cited alongside, same era.
A survey for in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui · 2022
Cited alongside, same era.
Investigating causal understanding in llms
Marius Hobbhahn, Tom Lieberum, and David Seiler · 2022
Cited alongside, same era.
Maieutic prompting: Logically consistent reasoning with recursive explanations, 2022
Jaehun Jung, Lianhui Qin, Sean Welleck, Faeze Brahman, Chandra Bhagavatula, Ronan Le Bras, and Yejin Choi · 2022
Cited alongside, same era.
Complexity-based prompting for multi-step reasoning, 2023
Yao Fu, Hao Peng, Ashish Sabharwal, Peter Clark, and Tushar Khot · 2023
Closest in time.
Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung · 2023
Closest in time.
Large language models are zero-shot reasoners, 2023
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2023
Closest in time.
Causal reasoning and large language models: Opening a new frontier for causality, 2023
Emre Kıcıman, Robert Ness, Amit Sharma, and Chenhao Tan · 2023
Closest in time.
Halueval: A large-scale hallucination evaluation benchmark for large language models
Junyi Li, Xiaoxue Cheng, Wayne Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen · 2023
Closest in time.
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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
Cited alongside, same era.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang (Shane) Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
Cited alongside, same era.
Holistic evaluation of language models, 2022
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel Orr, Lucia Zheng, Mert Yuksekgonul, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, and Yuta Koreeda · 2022
Cited alongside, same era.
Generated knowledge prompting for commonsense reasoning, 2022
Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, and Hannaneh Hajishirzi · 2022
Cited alongside, same era.
Challenging big-bench tasks and whether chain-of-thought can solve them, 2022
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V. Le, Ed H. Chi, Denny Zhou, and Jason Wei · 2022
Cited alongside, same era.
Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al · 2022
Cited alongside, same era.
Can foundation models talk causality?, 2022
Moritz Willig, Matej Zečević, Devendra Singh Dhami, and Kristian Kersting · 2022
Cited alongside, same era.
Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, and Chris Callison-Burch · 2023
Closest in time.
Self-refine: Iterative refinement with self-feedback, 2023
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark · 2023
Closest in time.
Sources of hallucination by large language models on inference tasks, 2023
Nick McKenna, Tianyi Li, Liang Cheng, Mohammad Javad Hosseini, Mark Johnson, and Mark Steedman · 2023
Closest in time.
Augmented language models: a survey, 2023
Grégoire Mialon, Roberto Dessì, Maria Lomeli, Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, Edouard Grave, Yann LeCun, and Thomas Scialom · 2023
Closest in time.
Gpt-4 technical report, 2023
OpenAI · 2023
Closest in time.
Art: Automatic multi-step reasoning and tool-use for large language models, 2023
Bhargavi Paranjape, Scott Lundberg, Sameer Singh, Hannaneh Hajishirzi, Luke Zettlemoyer, and Marco Tulio Ribeiro · 2023
Closest in time.
Reasoning with language model prompting: A survey, 2023
Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, and Huajun Chen · 2023
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A survey of hallucination in large foundation models, 2023
Vipula Rawte, Amit Sheth, and Amitava Das · 2023
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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
Closest in time.
Toolformer: Language models can teach themselves to use tools, 2023
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2023
Closest in time.
Algorithm of thoughts: Enhancing exploration of ideas in large language models, 2023
Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Lu Wang, Ruoxi Jia, and Ming Jin · 2023
Closest in time.
Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face, 2023
Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang · 2023
Closest in time.
Large language models can be easily distracted by irrelevant context, 2023
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed Chi, Nathanael Schärli, and Denny Zhou · 2023
Closest in time.
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
Closest in time.
From word models to world models: Translating from natural language to the probabilistic language of thought, 2023
Lionel Wong, Gabriel Grand, Alexander K. Lew, Noah D. Goodman, Vikash K. Mansinghka, Jacob Andreas, and Joshua B. Tenenbaum · 2023
Closest in time.
Decomposition enhances reasoning via self-evaluation guided decoding, 2023
Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, Xu Zhao, Min-Yen Kan, Junxian He, and Qizhe Xie · 2023
Closest in time.
Causal parrots: Large language models may talk causality but are not causal, 2023
Matej Zečević, Moritz Willig, Devendra Singh Dhami, and Kristian Kersting · 2023
Closest in time.