Fetching the paper…
Reading the bibliography…
While the emergence of powerful language models along with Chain-of-thought prompting has made automation more and more omnipresent, it sometimes demonstrates its weakness in long-term or multi-step logical reasoning.
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 · 1901
Earlier work this paper cites.
A learning algorithm for boltzmann machines
David H Ackley, Geoffrey E Hinton, and Terrence J Sejnowski · 1985
Earlier work this paper cites.
Incorporating diversity in active learning with support vector machines
Klaus Brinker · 2003
Earlier work this paper cites.
International Encyclopedia of Ergonomics and Human Factors, -3 Volume Set
Waldemar Karwowski · 2006
Earlier work this paper cites.
Learning to solve arithmetic word problems with verb categorization
Mohammad Javad Hosseini, Hannaneh Hajishirzi, Oren Etzioni, and Nate Kushman · 2014
Earlier work this paper cites.
Learning to automatically solve algebra word problems
Nate Kushman, Yoav Artzi, Luke Zettlemoyer, and Regina Barzilay · 2014
Earlier work this paper cites.
Intermediate microeconomics: a modern approach
Hal R. Varian · 2014
Earlier work this paper cites.
Parsing algebraic word problems into equations
Rik Koncel-Kedziorski, Hannaneh Hajishirzi, Ashish Sabharwal, Oren Etzioni, and Siena Dumas Ang · 2015
Earlier work this paper cites.
Multi-class active learning by uncertainty sampling with diversity maximization
Yi Yang, Zhigang Ma, Feiping Nie, Xiaojun Chang, and Alexander G Hauptmann · 2015
Earlier work this paper cites.
Solving general arithmetic word problems
Subhro Roy and Dan Roth · 2016
Earlier work this paper cites.
Controlling linguistic style aspects in neural language generation
Jessica Ficler and Yoav Goldberg · 2017
Earlier work this paper cites.
Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of mrnet
Nicholas Bien, Pranav Rajpurkar, Robyn L Ball, Jeremy Irvin, Allison Park, Erik Jones, Michael Bereket, Bhavik N Patel, Kristen W Yeom, Katie Shpanskaya, et al · 2018
Earlier work this paper cites.
Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin · 2018
Earlier work this paper cites.
Learning to write with cooperative discriminators
Ari Holtzman, Jan Buys, Maxwell Forbes, Antoine Bosselut, David Golub, and Yejin Choi · 2018
Cited alongside, same era.
Commonsenseqa: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant · 2018
Cited alongside, same era.
Mathqa: Towards interpretable math word problem solving with operation-based formalisms
Aida Amini, Saadia Gabriel, Peter Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi · 2019
Cited alongside, same era.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Ai in criminal law: An overview of ai applications in substantive and procedural criminal law
Bart Custers · 2022
Later among the works it cites.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
Later among the works it cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
Later among the works it cites.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same era.
Human-in-the-loop artificial intelligence
Fabio Massimo Zanzotto · 2019
Cited alongside, same era.
Contextual diversity for active learning
Sharat Agarwal, Himanshu Arora, Saket Anand, and Chetan Arora · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman · 2021
Cited alongside, same era.
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
Cited alongside, same era.
A diverse corpus for evaluating and developing english math word problem solvers
Shen-Yun Miao, Chao-Chun Liang, and Keh-Yih Su · 2021
Cited alongside, same era.
Are nlp models really able to solve simple math word problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal · 2021
Cited alongside, same era.
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, and Denny Zhou · 2022
Later among the works it cites.
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou · 2022
Later among the works it cites.
A survey of human-in-the-loop for machine learning
Xingjiao Wu, Luwei Xiao, Yixuan Sun, Junhang Zhang, Tianlong Ma, and Liang He · 2022
Later among the works it cites.
Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al · 2022
Later among the works it cites.
Ibm plans to replace 7,800 jobs with ai over time, pauses hiring certain positions, IBM CEO Arvind Krishna says he could see 30% of back-office functions replaced by AI over 5 years., 2023
BENJ EDWARDS · 2023
Closest in time.
Minerva: Solving quantitative reasoning problems with language models, 2023
Google Research · 2023
Closest in time.
Human-in-the-loop machine learning: A state of the art
Eduardo Mosqueira-Rey, Elena Hernández-Pereira, David Alonso-Ríos, José Bobes-Bascarán, and Ángel Fernández-Leal · 2023
Closest in time.
Compositional task representations for large language models
Nan Shao, Zefan Cai, Chonghua Liao, Yanan Zheng, Zhilin Yang, et al · 2023
Closest in time.