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Inductive reasoning - the process of inferring general rules from a small number of observations - is a fundamental aspect of human intelligence.
On the measure of intelligence
François Chollet. 2019 · 1911
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Gödel, Escher, Bach: An Eternal Golden Braid
Douglas Hofstadter. 1979 · 1979
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Core knowledge
Elizabeth S Spelke and Katherine D Kinzler. 2007 · 2007
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Driving semantic parsing from the world’s response
James Clarke, Dan Goldwasser, Ming-Wei Chang, and Dan Roth. 2010 · 2010
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Automating string processing in spreadsheets using input-output examples
Sumit Gulwani. 2011 · 2011
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A machine learning framework for programming by example
Aditya Menon, Omer Tamuz, Sumit Gulwani, Butler Lampson, and Adam Kalai. 2013 · 2013
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Human-level concept learning through probabilistic program induction
Brenden M. Lake, Ruslan Salakhutdinov, and Joshua B. Tenenbaum. 2015 · 2015
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Execution-guided neural program synthesis
Xinyun Chen, Chang Liu, and Dawn Song. 2019 · 2019
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Playgol: Learning programs through play
Andrew Cropper. 2019 · 2019
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020 · 2020
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Learning to represent programs with property signatures
Augustus Odena and Charles Sutton. 2020 · 2020
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The child as hacker : building more human-like models of learning
Joshua Stewart Rule. 2020 · 2020
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Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, and Charles Sutton. 2021 · 2021
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Spreadsheetcoder: Formula prediction from semi-structured context
Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, and Denny Zhou. 2021 · 2021
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Dreamcoder: bootstrapping inductive program synthesis with wake-sleep library learning
Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sablé-Meyer, Lucas Morales, Luke Hewitt, Luc Cary, Armando Solar-Lezama, and Joshua B. Tenenbaum. 2021 · 2021
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Abstraction and analogy-making in artificial intelligence
Melanie Mitchell. 2021 · 2021
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{BUSTLE}: Bottom-up program synthesis through learning-guided exploration
Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton, and Hanjun Dai. 2021 · 2021
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Playgrounds for abstraction and reasoning
Subin Kim, Prin Phunyaphibarn, Donghyun Ahn, and Sundong Kim. 2022 · 2022
Cited alongside, same era.
Crossbeam: Learning to search in bottom-up program synthesis
Kensen Shi, Hanjun Dai, Kevin Ellis, and Charles Sutton. 2022 · 2022
Cited alongside, same era.
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou. 2022 · 2022
Cited alongside, same era.
Flashfill++: Scaling programming by example by cutting to the chase
José Cambronero, Sumit Gulwani, Vu Le, Daniel Perelman, Arjun Radhakrishna, Clint Simon, and Ashish Tiwari. 2023 · 2023
Cited alongside, same era.
Human-like few-shot learning via bayesian reasoning over natural language
Kevin Ellis. 2023 · 2023
Cited alongside, same era.
Large language models cannot self-correct reasoning yet
Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Wei Yu, Xinying Song, and Denny Zhou. 2024 · 2024
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Can LLMs recognize toxicity? a structured investigation framework and toxicity metric
Hyukhun Koh, Dohyung Kim, Minwoo Lee, and Kyomin Jung. 2024 · 2024
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Training language models to self-correct via reinforcement learning
Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, Lei M Zhang, Kay McKinney, Disha Shrivastava, Cosmin Paduraru, George Tucker, Doina Precup, Feryal Behbahani, and Aleksandra Faust. 2024 · 2024
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Is programming by example solved by llms?
Wen-Ding Li and Kevin Ellis. 2024 · 2024
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Scattered forest search: Smarter code space exploration with llms
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Jack Lanchantin, Shubham Toshniwal, Jason Weston, Arthur Szlam, and Sainbayar Sukhbaatar. 2023 · 2023
Cited alongside, same era.
Weakly supervised semantic parsing with execution-based spurious program filtering
Kang-il Lee, Segwang Kim, and Kyomin Jung. 2023 · 2023
Cited alongside, same era.
Is your code generated by chatGPT really correct? rigorous evaluation of large language models for code generation
Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang. 2023 · 2023
Cited alongside, same era.
Large language models as general pattern machines
Suvir Mirchandani, Fei Xia, Pete Florence, brian ichter, Danny Driess, Montserrat Gonzalez Arenas, Kanishka Rao, Dorsa Sadigh, and Andy Zeng. 2023 · 2023
Cited alongside, same era.
The conceptarc benchmark: Evaluating understanding and generalization in the arc domain
Arseny Moskvichev, Victor Vikram Odouard, and Melanie Mitchell. 2023 · 2023
Cited alongside, same era.
Emergent analogical reasoning in large language models
Taylor Webb, Keith J Holyoak, and Hongjing Lu. 2023 · 2023
Cited alongside, same era.
Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan. 2023 · 2023
Cited alongside, same era.
Jonathan Light, Yue Wu, Yiyou Sun, Wenchao Yu, Yanchi liu, Xujiang Zhao, Ziniu Hu, Haifeng Chen, and Wei Cheng. 2024 · 2024
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Is self-repair a silver bullet for code generation?
Theo X. Olausson, Jeevana Priya Inala, Chenglong Wang, Jianfeng Gao, and Armando Solar-Lezama. 2024 · 2024
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Is temperature the creativity parameter of large language models?
Max Peeperkorn, Tom Kouwenhoven, Dan Brown, and Anna Jordanous. 2024 · 2024
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Phenomenal yet puzzling: Testing inductive reasoning capabilities of language models with hypothesis refinement
Linlu Qiu, Liwei Jiang, Ximing Lu, Melanie Sclar, Valentina Pyatkin, Chandra Bhagavatula, Bailin Wang, Yoon Kim, Yejin Choi, Nouha Dziri, and Xiang Ren. 2024 · 2024
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Scaling llm test-time compute optimally can be more effective than scaling model parameters
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. 2024 · 2024
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ItD: Large language models can teach themselves induction through deduction
Wangtao Sun, Haotian Xu, Xuanqing Yu, Pei Chen, Shizhu He, Jun Zhao, and Kang Liu. 2024 · 2024
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Code repair with llms gives an exploration-exploitation tradeoff
Hao Tang, Keya Hu, Jin Peng Zhou, Sicheng Zhong, Wei-Long Zheng, Xujie Si, and Kevin Ellis. 2024 · 2024
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Synthesize, partition, then adapt: Eliciting diverse samples from foundation models
Yeming Wen and Swarat Chaudhuri. 2024 · 2024
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Language models as inductive reasoners
Zonglin Yang, Li Dong, Xinya Du, Hao Cheng, Erik Cambria, Xiaodong Liu, Jianfeng Gao, and Furu Wei. 2024 · 2024
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Take a step back: Evoking reasoning via abstraction in large language models
Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen, Heng-Tze Cheng, Ed H. Chi, Quoc V Le, and Denny Zhou. 2024 · 2024
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Paraphrase and solve: Exploring and exploiting the impact of surface form on mathematical reasoning in large language models
Yue Zhou, Yada Zhu, Diego Antognini, Yoon Kim, and Yang Zhang. 2024 · 2024
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Case2Code: Scalable synthetic data for code generation
Yunfan Shao, Linyang Li, Yichuan Ma, Peiji Li, Demin Song, Qinyuan Cheng, Shimin Li, Xiaonan Li, Pengyu Wang, Qipeng Guo, Hang Yan, Xipeng Qiu, Xuanjing Huang, and Dahua Lin. 2025 · 2025
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