Large Language Models for Mathematical Reasoning: Progresses and Challenges
CoRR(2024)
摘要
Mathematical reasoning serves as a cornerstone for assessing the fundamental
cognitive capabilities of human intelligence. In recent times, there has been a
notable surge in the development of Large Language Models (LLMs) geared towards
the automated resolution of mathematical problems. However, the landscape of
mathematical problem types is vast and varied, with LLM-oriented techniques
undergoing evaluation across diverse datasets and settings. This diversity
makes it challenging to discern the true advancements and obstacles within this
burgeoning field. This survey endeavors to address four pivotal dimensions: i)
a comprehensive exploration of the various mathematical problems and their
corresponding datasets that have been investigated; ii) an examination of the
spectrum of LLM-oriented techniques that have been proposed for mathematical
problem-solving; iii) an overview of factors and concerns affecting LLMs in
solving math; and iv) an elucidation of the persisting challenges within this
domain. To the best of our knowledge, this survey stands as one of the first
extensive examinations of the landscape of LLMs in the realm of mathematics,
providing a holistic perspective on the current state, accomplishments, and
future challenges in this rapidly evolving field.
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