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Reasoning is a fundamental aspect of human intelligence that plays a crucial role in activities such as problem solving, decision making, and critical thinking.
Roberta: A robustly optimized bert pretraining approach
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Reasoning about knowledge
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Logic in Computer Science: Modelling and reasoning about systems
Michael Huth and Mark Ryan. 2004 · 2004
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An introduction to legal reasoning
Edward H Levi. 2013 · 2013
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Compositional semantic parsing on semi-structured tables
Panupong Pasupat and Percy Liang. 2015 · 2015
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Solving general arithmetic word problems
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. 2017 · 2017
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Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
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Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Brenden M. Lake and Marco Baroni. 2018 · 2018
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What is reasoning?
Conor McHugh and Jonathan Way. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. 2018 · 2018
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The web as a knowledge-base for answering complex questions
Alon Talmor and Jonathan Berant. 2018 · 2018
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MathQA: Towards interpretable math word problem solving with operation-based formalisms
Aida Amini, Saadia Gabriel, Shanchuan Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi. 2019 · 2019
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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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Multi-hop reading comprehension through question decomposition and rescoring
Sewon Min, Victor Zhong, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
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Climbing towards NLU: On meaning, form, and understanding in the age of data
Emily M. Bender and Alexander Koller. 2020 · 2020
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Logical reasoning in formal and everyday reasoning tasks
Hugo Bronkhorst, Gerrit Roorda, Cor Suhre, and Martin Goedhart. 2020 · 2020
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared 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 M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher 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 · 2020
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Measuring compositional generalization: A comprehensive method on realistic data
Daniel Keysers, Nathanael Schärli, Nathan Scales, Hylke Buisman, Daniel Furrer, Sergii Kashubin, Nikola Momchev, Danila Sinopalnikov, Lukasz Stafiniak, Tibor Tihon, Dmitry Tsarkov, Xiao Wang, Marc van Zee, and Olivier Bousquet. 2020 · 2020
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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
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A diverse corpus for evaluating and developing English math word problem solvers
Shen-yun Miao, Chao-Chun Liang, and Keh-Yih Su. 2020 · 2020
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Unsupervised question decomposition for question answering
Ethan Perez, Patrick Lewis, Wen-tau Yih, Kyunghyun Cho, and Douwe Kiela. 2020 · 2020
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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, Peter J Liu, et al. 2020 · 2020
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Deep learning needs a prefrontal cortex
Jacob Russin, Randall C O’Reilly, and Yoshua Bengio. 2020 · 2020
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Leap-of-thought: Teaching pre-trained models to systematically reason over implicit knowledge
Alon Talmor, Oyvind Tafjord, Peter Clark, Yoav Goldberg, and Jonathan Berant. 2020 · 2020
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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
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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
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021 · 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 · 2021
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Teaching small language models to reason
Lucie Charlotte Magister, Jonathan Mallinson, Jakub Adamek, Eric Malmi, and Aliaksei Severyn. 2022 · 2022
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Human language understanding & reasoning
Christopher D Manning. 2022 · 2022
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A property induction framework for neural language models
Kanishka Misra, Julia Taylor Rayz, and Allyson Ettinger. 2022 · 2022
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FeTaQA: Free-form table question answering
Linyong Nan, Chiachun Hsieh, Ziming Mao, Xi Victoria Lin, Neha Verma, Rui Zhang, Wojciech Kryściński, Hailey Schoelkopf, Riley Kong, Xiangru Tang, Mutethia Mutuma, Ben Rosand, Isabel Trindade, Renusree Bandaru, Jacob Cunningham, Caiming Xiong, Dragomir Radev, and Dragomir Radev. 2022 · 2022
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Show your work: Scratchpads for intermediate computation with language models
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, and Augustus Odena. 2022 · 2022
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Reasoning with transformer-based models: Deep learning, but shallow reasoning
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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
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Implicit representations of meaning in neural language models
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Abstraction and analogy-making in artificial intelligence
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Scaling language models: Methods, analysis & insights from training gopher
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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
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Commonsense knowledge reasoning and generation with pre-trained language models: A survey
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Large language models are few (1)-shot table reasoners
Wenhu Chen. 2022 · 2022
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Chatgpt: Optimizing language models for dialogue
OpenAI. 2022 · 2022
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Reasoning like program executors
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Meaning without reference in large language models
Steven T Piantasodi and Felix Hill. 2022 · 2022
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Measuring and narrowing the compositionality gap in language models
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Ben Prystawski, Paul Thibodeau, and Noah Goodman. 2022 · 2022
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Reasoning with language model prompting: A survey
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Impact of pretraining term frequencies on few-shot reasoning
Yasaman Razeghi, Robert L Logan IV, Matt Gardner, and Sameer Singh. 2022 · 2022
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Large language models are not zero-shot communicators
Laura Ruis, Akbir Khan, Stella Biderman, Sara Hooker, Tim Rocktäschel, and Edward Grefenstette. 2022 · 2022
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Language models are greedy reasoners: A systematic formal analysis of chain-of-thought
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Bloom: A 176b-parameter open-access multilingual language model
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Language models are multilingual chain-of-thought reasoners
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Kumar Shridhar, Alessandro Stolfo, and Mrinmaya Sachan. 2022 · 2022
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Llm-planner: Few-shot grounded planning for embodied agents with large language models
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
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Challenging big-bench tasks and whether chain-of-thought can solve them
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Galactica: A large language model for science
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Large language models still can’t plan (a benchmark for llms on planning and reasoning about change)
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Large language models are reasoners with self-verification
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Reframing human-AI collaboration for generating free-text explanations
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Language models as inductive reasoners
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The unreliability of explanations in few-shot prompting for textual reasoning
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Alert: Adapting language models to reasoning tasks
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STar: Bootstrapping reasoning with reasoning
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