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LLMs have performed well on several reasoning benchmarks, including ones that test analogical reasoning abilities.
Progressive Matrices Test: A perceptual test of intelligence: Individual form
J. C. Raven · 1938
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Douglas R. Hofstadter · 1985
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Dedre Gentner, Mary Jo Rattermann, and Kenneth D. Forbus · 1993
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Melanie Mitchell · 1993
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Douglas R. Hofstadter and Melanie Mitchell · 1994
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Valerie Thompson · 2009
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John G. Geake and Peter C. Hansen · 2010
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Recreating Raven’s: Software for systematically generating large numbers of Raven-like matrix problems with normed properties
Laura E Matzen, Zachary O Benz, Kevin R Dixon, Jamie Posey, James K Kroger, and Ann E Speed · 2010
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Event-related potential responses to letter-string comparison analogies
Changquan Long, Jing Li, Antao Chen, Jiang Qiu, Jie Chen, and Hong Li · 2015
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Understanding the source of semantic regularities in word embeddings
Hsiao-Yu Chiang, Jose Camacho-Collados, and Zachary Pardos · 2020
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Shortcutted commonsense: Data spuriousness in deep learning of commonsense reasoning
Ruben Branco, António Branco, Joao Rodrigues, and Joao Silva · 2021
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Towards reasoning in large language models: A survey
Jie Huang and Kevin Chen-Chuan Chang · 2022
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Impact of pretraining term frequencies on few-shot numerical reasoning
Yasaman Razeghi, Robert L. Logan IV, Matt Gardner, and Sameer Singh · 2022
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Leaping across the mental canyon: Higher-order long-distance analogical retrieval
Shir Dekel, Bruce Burns, and Micah Goldwater · 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, Soumya Sanyal, Sean Welleck, Xiang. Ren, Allyson. Ettinger, Zaid Harchaoui, and Yejin Choi · 2023
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Baby steps in evaluating the capacities of large language models
Michael C. Frank · 2023
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Response: Emergent analogical reasoning in large language models
Damian Hodel and Jevin West · 2023
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Language models, like humans, show content effects on reasoning tasks
Andrew K. Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan, Hannah R. Sheahan, Antonia Creswell, Dharshan Kumaran, James L. McClelland, and Felix Hill · 2024
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GSM-symbolic: Understanding the limitations of mathematical reasoning in large language models
Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, Oncel Tuzel, Samy Bengio, and Mehrdad Farajtabar · 2024
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Beyond accuracy: Evaluating the reasoning behavior of large language models: A survey
Philipp Mondorf and Barbara Plank · 2024
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Marianna Nezhurina, Lucia Cipolina-Kun, Mehdi Cherti, and Jenia Jitsev · 2024
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Learning to reason with LLMs, 2024
OpenAI · 2024
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Anna A. Ivanova · 2023
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Can LLMs really reason and plan?
Subbarao Kambhampati · 2023
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Emergent analogical reasoning in large language models
Taylor Webb, Keith J. Holyoak, and Hongjing Lu · 2023
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Pengfei Hong, Deepanway Ghosal, Navonil Majumder, Somak Aditya, Rada Mihalcea, and Soujanya Poria · 2024
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A peek into token bias: Large language models are not yet genuine reasoners
Bowen Jiang, Yangxinyu Xie, Zhuoqun Hao, Xiaomeng Wang, Tanwi Mallick, Weijie J. Su, Camillo J. Taylor, and Dan Roth · 2024
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Imagining and building wise machines: The centrality of ai metacognition
Samuel G. B. Johnson, Amir-Hossein Karimi, Yoshua Bengio, Nick Chater, Tobias Gerstenberg, Kate Larson, Sydney Levine, Melanie Mitchell, Iyad Rahwan, Bernhard Schölkopf, et al · 2024
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Akshara Prabhakar, Thomas L. Griffiths, and R. Thomas McCoy · 2024
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ARN: Analogical reasoning on narratives
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Functional benchmarks for robust evaluation of reasoning performance, and the reasoning gap
Saurabh Srivastava, Annarose M. B., Anto P. V., Shashank Menon, Ajay Sukumar, Alan Philipose, Stevin Prince, and Sooraj Thomas · 2024
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Can large language models generalize analogy solving like people can?
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