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Large language models (LLMs) have demonstrated impressive capabilities in mathematical problem solving, particularly in single turn question answering formats.
Natural language input for a computer problem-solving system
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Large language models are zero-shot reasoners
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An empirical study on challenging math problem solving with gpt-4
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Chain-of-thought prompting elicits reasoning in large language models
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Enhancing chat language models by scaling high-quality instructional conversations
Ning Ding, Yulin Chen, Bokai Xu, Yujia Qin, Zhi Zheng, Shengding Hu, Zhiyuan Liu, Maosong Sun, and Bowen Zhou · 2023
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Large language models cannot self-correct reasoning yet
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al · 2023
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Learning by analogy: Diverse questions generation in math word problem
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Llemma: An open language model for mathematics
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Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts
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Deepseekmath: Pushing the limits of mathematical reasoning in open language models
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Gemma: Open models based on gemini research and technology
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Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems?
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