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Large Language Models (LLMs) significantly benefit from Chain-of-Thought (CoT) prompting in performing various reasoning tasks.
Learning to solve arithmetic word problems with verb categorization
Mohammad Javad Hosseini, Hannaneh Hajishirzi, Oren Etzioni, and Nate Kushman · 2014
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Multitask prompted training enables zero-shot task generalization
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Generate & rank: A multi-task framework for math word problems
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Natural language deduction through search over statement compositions
Kaj Bostrom, Zayne Sprague, Swarat Chaudhuri, and Greg Durrett · 2022
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Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen · 2022
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Palm: Scaling language modeling with pathways
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Faithful reasoning using large language models
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Compositional semantic parsing with large language models
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Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al · 2022
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Large language models are zero-shot reasoners
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Can language models learn from explanations in context?
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Learn to explain: Multimodal reasoning via thought chains for science question answering
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Few-shot self-rationalization with natural language prompts, 2022
Least-to-most prompting enables complex reasoning in large language models
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Teaching algorithmic reasoning via in-context learning
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Training language models to follow instructions with human feedback
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Bloom: A 176b-parameter open-access multilingual language model
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Language models are multilingual chain-of-thought reasoners
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Self-consistency improves chain of thought reasoning in language models
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Emergent abilities of large language models
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