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Although Large Language Models (LLMs) are showing impressive performance on a wide range of Natural Language Processing tasks, researchers have found that they still have limited ability to conduct induction.
On the measure of intelligence
François Chollet. 2019 · 1911
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The problem of induction
Steven A Sloman and David Lagnado. 2005 · 2005
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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 · 2017
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The child as hacker: building more human-like models of learning
Joshua Stewart Rule. 2020 · 2020
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A large-scale benchmark for few-shot program induction and synthesis
Ferran Alet, Javier Lopez-Contreras, James Koppel, Maxwell Nye, Armando Solar-Lezama, Tomas Lozano-Perez, Leslie Kaelbling, and Joshua Tenenbaum. 2021 · 2021
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
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Neural, symbolic and neural-symbolic reasoning on knowledge graphs
Jing Zhang, Bo Chen, Lingxi Zhang, Xirui Ke, and Haipeng Ding. 2021 · 2021
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Instruction induction: From few examples to natural language task descriptions
Or Honovich, Uri Shaham, Samuel R Bowman, and Omer Levy. 2022 · 2022
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022 · 2022
Cited alongside, same era.
Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, et al. 2023 · 2023
Cited alongside, same era.
Qlora: Efficient finetuning of quantized llms
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023 · 2023
Cited alongside, same era.
Large language models are not abstract reasoners
Gaël Gendron, Qiming Bao, Michael Witbrock, and Gillian Dobbie. 2023 · 2023
Cited alongside, same era.
Linlu Qiu, Liwei Jiang, Ximing Lu, Melanie Sclar, Valentina Pyatkin, Chandra Bhagavatula, Bailin Wang, Yoon Kim, Yejin Choi, Nouha Dziri, et al. 2023 · 2023
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Naive bayes-based context extension
Jianlin Su. 2023 · 2023
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Expnote: Black-box large language models are better task solvers with experience notebook
Wangtao Sun, Xuanqing Yu, Shizhu He, Jun Zhao, and Kang Liu. 2023 · 2023
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Large language models are in-context semantic reasoners rather than symbolic reasoners
Xiaojuan Tang, Zilong Zheng, Jiaqi Li, Fanxu Meng, Song-Chun Zhu, Yitao Liang, and Muhan Zhang. 2023 · 2023
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Jerzy W Grzymala-Busse. 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.
Comparing humans, gpt-4, and gpt-4v on abstraction and reasoning tasks
Melanie Mitchell, Alessandro B Palmarini, and Arseny Moskvichev. 2023 · 2023
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Guideline learning for in-context information extraction
Chaoxu Pang, Yixuan Cao, Qiang Ding, and Ping Luo. 2023 · 2023
Cited alongside, same era.
Questions concerning certain faculties claimed for man
Charles S Peirce. 1868
Cited in the paper.
Ruocheng Wang, Eric Zelikman, Gabriel Poesia, Yewen Pu, Nick Haber, and Noah D Goodman. 2023 · 2023
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Failures pave the way: Enhancing large language models through tuning-free rule accumulation
Zeyuan Yang, Peng Li, and Yang Liu. 2023 · 2023
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Expel: Llm agents are experiential learners
Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, and Gao Huang. 2023 · 2023
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Large language models can learn rules
Zhaocheng Zhu, Yuan Xue, Xinyun Chen, Denny Zhou, Jian Tang, Dale Schuurmans, and Hanjun Dai. 2023 · 2023
Later among the works it cites.