Fetching the paper…
Reading the bibliography…
Remarkable progress has been made on automated reasoning with natural text, by using Language Models (LMs) and methods such as Chain-of-Thought and Selection-Inference.
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
Earlier work this paper cites.
Programs with common sense
John McCarthy. 1959 · 1959
Earlier work this paper cites.
Planner: A language for proving theorems in robots
Carl Hewitt. 1969 · 1969
Earlier work this paper cites.
The next decade in AI: four steps towards robust artificial intelligence
Gary Marcus. 2020 · 2002
Earlier work this paper cites.
Artificial Intelligence: foundations of computational agents
David L Poole and Alan K Mackworth. 2010 · 2010
Earlier work this paper cites.
Neurosymbolic ai: the 3rd wave
Artur d’Avila Garcez and Luis C Lamb. 2020 · 2012
Earlier work this paper cites.
Recognizing textual entailment: Models and applications
Ido Dagan, Dan Roth, Mark Sammons, and Fabio Massimo Zanzotto. 2013 · 2013
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
Earlier work this paper cites.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
Earlier work this paper cites.
Measuring systematic generalization in neural proof generation with transformers
Nicolas Gontier, Koustuv Sinha, Siva Reddy, and Chris Pal. 2020 · 2020
Earlier work this paper cites.
Learning to explain: Datasets and models for identifying valid reasoning chains in multihop question-answering
Harsh Jhamtani and Peter Clark. 2020 · 2020
Earlier work this paper cites.
Are pretrained language models symbolic reasoners over knowledge?
Nora Kassner, Benno Krojer, and Hinrich Schütze. 2020 · 2020
Earlier work this paper cites.
Critical thinking for language models
Gregor Betz, Christian Voigt, and Kyle Richardson. 2021 · 2021
Earlier work this paper cites.
Transformers as soft reasoners over language
Peter Clark, Oyvind Tafjord, and Kyle Richardson. 2021 · 2021
Earlier work this paper cites.
Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021 · 2021
Earlier work this paper cites.
Explaining answers with entailment trees
Bhavana Dalvi, Peter Jansen, Oyvind Tafjord, Zhengnan Xie, Hannah Smith, Leighanna Pipatanangkura, and Peter Clark. 2021 · 2021
Cited alongside, same era.
Measuring mathematical problem solving with the math dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021 · 2021
Cited alongside, same era.
Scaling language models: Methods, analysis & insights from training Gopher
Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen Huang, Amelia Glaese, Johannes Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Uesato, John Mellor, Irina Higgins, Antonia Creswell, Nat McAleese, Amy Wu, Erich Elsen, Siddhant Jayakumar, Elena Buchatskaya, David Budden, Esme Sutherland, Karen Simonyan, Michela Paganini, Laurent Sifre, Lena Martens, Xiang Lorraine Li, Adhiguna Kuncoro, Aida Nematzadeh, Elena Gribovskaya, Domenic Donato, Angeliki Lazaridou, Arthur Mensch, Jean-Baptiste Lespiau, Maria Tsimpoukelli, Nikolai Grigorev, Doug Fritz, Thibault Sottiaux, Mantas Pajarskas, Toby Pohlen, Zhitao Gong, Daniel Toyama, Cyprien de Masson d’Autume, Yujia Li, Tayfun Terzi, Vladimir Mikulik, Igor Babuschkin, Aidan Clark, Diego de Las Casas, Aurelia Guy, Chris Jones, James Bradbury, Matthew Johnson, Blake Hechtman, Laura Weidinger, Iason Gabriel, William Isaac, Ed Lockhart, Simon Osindero, Laura Rimell, Chris Dyer, Oriol Vinyals, Kareem Ayoub, Jeff Stanway, Lorrayne Bennett, Demis Hassabis, Koray Kavukcuoglu, and Geoffrey Irving. 2021 · 2021
Cited alongside, same era.
Challenging big-bench tasks and whether chain-of-thought can solve them
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou, et al. 2022 · 2022
Closest in time.
Large language models still can’t plan (a benchmark for LLMs on planning and reasoning about change)
Karthik Valmeekam, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati. 2022 · 2022
Closest in time.
Iteratively prompt pre-trained language models for chain of thought
Boshi Wang, Xiang Deng, and Huan Sun. 2022 · 2022
Closest in time.
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 · 2022
Closest in time.
STaR: Bootstrapping reasoning with reasoning
Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah Goodman. 2022 · 2022
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
RuleBERT: Teaching soft rules to pre-trained language models
Mohammed Saeed, Naser Ahmadi, Preslav Nakov, and Paolo Papotti. 2021 · 2021
Cited alongside, same era.
Generate & rank: A multi-task framework for math word problems
Jianhao Shen, Yichun Yin, Lin Li, Lifeng Shang, Xin Jiang, Ming Zhang, and Qun Liu. 2021 · 2021
Cited alongside, same era.
ProofWriter: Generating implications, proofs, and abductive statements over natural language
Oyvind Tafjord, Bhavana Dalvi, and Peter Clark. 2021 · 2021
Cited alongside, same era.
Exploring length generalization in large language models
Cem Anil, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, and Behnam Neyshabur. 2022 · 2022
Cited alongside, same era.
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, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. 2022 · 2022
Cited alongside, same era.
Faithful reasoning using large language models
Antonia Creswell and Murray Shanahan. 2022 · 2022
Cited alongside, same era.
Successive prompting for decomposing complex questions
Dheeru Dua, Shivanshu Gupta, Sameer Singh, and Matt Gardner. 2022 · 2022
Cited alongside, same era.
FOLIO: Natural language reasoning with first-order logic
Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Luke Benson, Lucy Sun, Ekaterina Zubova, Yujie Qiao, Matthew Burtell, et al. 2022 · 2022
Cited alongside, same era.
Towards reasoning in large language models: A survey
Jie Huang and Kevin Chen-Chuan Chang. 2022 · 2022
Cited alongside, same era.
Improved logical reasoning of language models via differentiable symbolic programming
Hanlin Zhang, Ziyang Li, Jiani Huang, Mayur Naik, and Eric Xing. 2022a · 2022
Closest in time.
Teaching algorithmic reasoning via in-context learning
Hattie Zhou, Azade Nova, Hugo Larochelle, Aaron Courville, Behnam Neyshabur, and Hanie Sedghi. 2022 · 2022
Closest in time.
Selection-inference: Exploiting large language models for interpretable logical reasoning
Antonia Creswell, Murray Shanahan, and Irina Higgins. 2023 · 2023
Closest in time.
Understanding finetuning for factual knowledge extraction from language models
Mehran Kazemi, Sid Mittal, and Deepak Ramachandran. 2023 · 2023
Closest in time.
Decomposed prompting: A modular approach for solving complex tasks
Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, and Ashish Sabharwal. 2023 · 2023
Closest in time.
Transformers learn shortcuts to automata
Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, and Cyril Zhang. 2023 · 2023
Closest in time.
Language models are greedy reasoners: A systematic formal analysis of chain-of-thought
Abulhair Saparov and He He. 2023 · 2023
Closest in time.
Imanol Schlag, Sainbayar Sukhbaatar, Asli Celikyilmaz, Wen-tau Yih, Jason Weston, Jürgen Schmidhuber, and Xian Li. 2023 · 2023
Closest in time.
Least-to-most prompting enables complex reasoning in large language models
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V Le, and Ed H. Chi. 2023 · 2023
Closest in time.