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We introduce MAmmoTH, a series of open-source large language models (LLMs) specifically tailored for general math problem-solving.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al · 1910
Earlier work this paper cites.
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Training verifiers to solve math word problems
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Advancing mathematics by guiding human intuition with ai
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Measuring mathematical problem solving with the math dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt · 2021
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Are NLP models really able to solve simple math word problems?
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CrossFit: A few-shot learning challenge for cross-task generalization in NLP
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Constitutional ai: Harmlessness from ai feedback
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Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen · 2022
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Large language models are zero-shot reasoners
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Solving quantitative reasoning problems with language models
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Language models of code are few-shot commonsense learners
Aman Madaan, Shuyan Zhou, Uri Alon, Yiming Yang, and Graham Neubig · 2022
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LILA: A unified benchmark for mathematical reasoning
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NumGLUE: A suite of fundamental yet challenging mathematical reasoning tasks
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Multitask prompted training enables zero-shot task generalization
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The flan collection: Designing data and methods for effective instruction tuning
Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V Le, Barret Zoph, Jason Wei, et al · 2023
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Super-NaturalInstructions: Generalization via declarative instructions on 1600+ NLP tasks
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Finetuned language models are zero-shot learners
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Code llama: Open foundation models for code
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Simple synthetic data reduces sycophancy in large language models
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Gpt can solve mathematical problems without a calculator
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