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A Survey of Deep Learning for Mathematical Reasoning

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arxiv 2212.10535 v2 pith:SSOE3XGN submitted 2022-12-20 cs.AI cs.CLcs.CVcs.LG

A Survey of Deep Learning for Mathematical Reasoning

classification cs.AI cs.CLcs.CVcs.LG
keywords learningreasoningdeepmathematicaladvancesbenchmarksfieldsintelligence
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Mathematical reasoning is a fundamental aspect of human intelligence and is applicable in various fields, including science, engineering, finance, and everyday life. The development of artificial intelligence (AI) systems capable of solving math problems and proving theorems has garnered significant interest in the fields of machine learning and natural language processing. For example, mathematics serves as a testbed for aspects of reasoning that are challenging for powerful deep learning models, driving new algorithmic and modeling advances. On the other hand, recent advances in large-scale neural language models have opened up new benchmarks and opportunities to use deep learning for mathematical reasoning. In this survey paper, we review the key tasks, datasets, and methods at the intersection of mathematical reasoning and deep learning over the past decade. We also evaluate existing benchmarks and methods, and discuss future research directions in this domain.

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Cited by 5 Pith papers

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  3. Arrows of Math Reasoning Data Synthesis for Large Language Models: Diversity, Complexity and Correctness

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    A program-assisted pipeline generates 12.3 million math problem-solution pairs with execution-based verification, and fine-tuning on a 50k sample improves model scores on GSM8K, MATH, Minerva, and SVAMP.

  4. A Survey of Large Language Models

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    This survey reviews the background, key techniques, and evaluation methods for large language models, emphasizing emergent abilities that appear at large scales.

  5. Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning

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