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The ability to derive underlying principles from a handful of observations and then generalize to novel situations -- known as inductive reasoning -- is central to human intelligence.
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Children’s causal inferences from indirect evidence: Backwards blocking and bayesian reasoning in preschoolers
David M Sobel, Joshua B Tenenbaum, and Alison Gopnik · 2004
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Rapid responding increases belief bias: Evidence for the dual-process theory of reasoning
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Quasi-synchronous grammars: Alignment by soft projection of syntactic dependencies
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Word learning as bayesian inference
Fei Xu and Joshua B Tenenbaum · 2007
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A rational analysis of rule-based concept learning
Noah D Goodman, Joshua B Tenenbaum, Jacob Feldman, and Thomas L Griffiths · 2008
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Structured statistical models of inductive reasoning
Charles Kemp and Joshua B Tenenbaum · 2009
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How to grow a mind: Statistics, structure, and abstraction
Joshua B. Tenenbaum, Charles Kemp, Thomas L. Griffiths, and Noah D. Goodman · 2011
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From sensory signals to modality-independent conceptual representations: A probabilistic language of thought approach
Goker Erdogan, Ilker Yildirim, and Robert A Jacobs · 2015
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Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
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The logical primitives of thought: Empirical foundations for compositional cognitive models
Steven T Piantadosi, Joshua B Tenenbaum, and Noah D Goodman · 2016
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Building machines that learn and think like people
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman · 2017
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Online learning of symbolic concepts
Pratiksha Thaker, Joshua B Tenenbaum, and Samuel J Gershman · 2017
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Learning with latent language
Jacob Andreas, Dan Klein, and Sergey Levine · 2018
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Grounding compositional hypothesis generation in specific instances
Neil Bramley, Anselm Rothe, Josh Tenenbaum, Fei Xu, and Todd Gureckis · 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
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Human few-shot learning of compositional instructions
Brenden M. Lake, Tal Linzen, and Marco Baroni · 2019
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Bayesian synthesis of probabilistic programs for automatic data modeling
Feras A Saad, Marco F Cusumano-Towner, Ulrich Schaechtle, Martin C Rinard, and Vikash K Mansinghka · 2019
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Shaping visual representations with language for few-shot classification
Jesse Mu, Percy Liang, and Noah Goodman · 2020
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Learning compositional rules via neural program synthesis
Maxwell I. Nye, Armando Solar-Lezama, Josh Tenenbaum, and Brenden M. Lake · 2020
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The child as hacker: building more human-like models of learning
Joshua Stewart Rule · 2020
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Learning abstract structure for drawing by efficient motor program induction
Lucas Y. Tian, Kevin Ellis, Marta Kryven, and Josh Tenenbaum · 2020
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Lexicon learning for few shot sequence modeling
Ekin Akyurek and Jacob Andreas · 2021
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A large-scale benchmark for few-shot program induction and synthesis
Ferran Alet, Javier Lopez-Contreras, James Koppel, Maxwell I. Nye, Armando Solar-Lezama, Tomás Lozano-Pérez, Leslie Pack Kaelbling, and Joshua B. Tenenbaum · 2021
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Documenting large webtext corpora: A case study on the colossal clean crawled corpus
Claude 2, 2023
Anthropic · 2023
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
BIG bench authors · 2023
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Faith and fate: Limits of transformers on compositionality
Nouha Dziri, Ximing Lu, Melanie Sclar, Xiang Lorraine Li, Liwei Jian, Bill Yuchen Lin, Peter West, Chandra Bhagavatula, Ronan Le Bras, Jena D Hwang, et al · 2023
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Modeling human-like concept learning with bayesian inference over natural language
Kevin Ellis · 2023
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Dreamcoder: growing generalizable, interpretable knowledge with wake–sleep bayesian program learning
Kevin Ellis, Lionel Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lore Anaya Pozo, Luke Hewitt, Armando Solar-Lezama, and Joshua B Tenenbaum · 2023
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Jesse Dodge, Maarten Sap, Ana Marasović, William Agnew, Gabriel Ilharco, Dirk Groeneveld, Margaret Mitchell, and Matt Gardner · 2021
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Fast and flexible: Human program induction in abstract reasoning tasks
Aysja Johnson, Wai Keen Vong, Brenden M Lake, and Todd M Gureckis · 2021
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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, et al · 2021
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ACRE: abstract causal reasoning beyond covariation
Chi Zhang, Baoxiong Jia, Mark Edmonds, Song-Chun Zhu, and Yixin Zhu · 2021
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Communicating natural programs to humans and machines
Sam Acquaviva, Yewen Pu, Marta Kryven, Theodoros Sechopoulos, Catherine Wong, Gabrielle Ecanow, Maxwell Nye, Michael Tessler, and Josh Tenenbaum · 2022
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Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen · 2022
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Language models show human-like content effects on reasoning
Ishita Dasgupta, Andrew K Lampinen, Stephanie CY Chan, Antonia Creswell, Dharshan Kumaran, James L McClelland, and Felix Hill · 2022
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Pal: Program-aided language models
Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig · 2023
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Large language models are not abstract reasoners
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Inductive reasoning in humans and large language models
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Instruction induction: From few examples to natural language task descriptions
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Large language models as general pattern machines
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Comparing humans, gpt-4, and gpt-4v on abstraction and reasoning tasks
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The conceptarc benchmark: Evaluating understanding and generalization in the arc domain
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Demystifying gpt self-repair for code generation
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Gpt-4 technical report, 2023
OpenAI · 2023
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Large language models are in-context semantic reasoners rather than symbolic reasoners
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Self-consistency improves chain of thought reasoning in language models
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Emergent analogical reasoning in large language models
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The generative ai paradox: “what it can create, it may not understand”
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Lionel Wong, Gabriel Grand, Alexander K Lew, Noah D Goodman, Vikash K Mansinghka, Jacob Andreas, and Joshua B Tenenbaum · 2023
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Goal driven discovery of distributional differences via language descriptions
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