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Pre-training on large-scale, high-quality datasets is crucial for enhancing the reasoning capabilities of Large Language Models (LLMs), especially in specialized domains such as mathematics.
On the resemblance and containment of documents
Andrei Z Broder · 1997
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Common crawl - open repository of web crawl data
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Training verifiers to solve math word problems, 2021
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman · 2021
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Naturalproofs: Mathematical theorem proving in natural language
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Learn to explain: Multimodal reasoning via thought chains for science question answering
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Chartqa: A benchmark for question answering about charts with visual and logical reasoning, 2022
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Chain-of-thought prompting elicits reasoning in large language models
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Llemma: An open language model for mathematics, 2023
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Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning
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Large language models are fixated by red herrings: Exploring creative problem solving and einstellung effect using the only connect wall dataset, 2023
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Openwebmath: An open dataset of high-quality mathematical web text
Keiran Paster, Marco Dos Santos, Zhangir Azerbayev, and Jimmy Ba · 2023
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Openwebmath: An open dataset of high-quality mathematical web text, 2023
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Generative ai for math: Part i – mathpile: A billion-token-scale pretraining corpus for math
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Deepseekmath: Pushing the limits of mathematical reasoning in open language models, 2024
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Math-llava: Bootstrapping mathematical reasoning for multimodal large language models, 2024
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Dart-math: Difficulty-aware rejection tuning for mathematical problem-solving
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Internlm-math: Open math large language models toward verifiable reasoning
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Geoeval: Benchmark for evaluating llms and multi-modal models on geometry problem-solving, 2024
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