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Large Language Models (LLMs) have limited performance when solving arithmetic reasoning tasks and often provide incorrect answers.
24. individual differences in reasoning: Implications for the rationality debate?
Keith E Stanovich and Richard F West · 2000
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Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh · 2020
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
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Prompt programming for large language models: Beyond the few-shot paradigm
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Large language models are zero-shot reasoners
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
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Are nlp models really able to solve simple math word problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal · 2021
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Scaling language models: Methods, analysis & insights from training gopher
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, and Denny Zhou · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou · 2022
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
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