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Large language models (LLMs) have showcased remarkable capabilities in complex reasoning through chain of thought (CoT) prompting.
Language models are few-shot learners
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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 · 2021
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Distilling linguistic context for language model compression
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
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Teaching small language models to reason
Lucie Charlotte Magister, Jonathan Mallinson, Jakub Adamek, Eric Malmi, and Aliaksei Severyn. 2022 · 2022
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Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
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Can rationalization improve robustness?
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When can models learn from explanations? a formal framework for understanding the roles of explanation data
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Large language models are reasoning teachers
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Training compute-optimal large language models
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Chain of thought prompting elicits reasoning in large language models
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Are NLP models really able to solve simple math word problems?
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