【Proposal】MathLLaMA: A Specialized Language Model for Mathematical Reasoning and Problem-Solving
Keywords: Machine learning, Large Language Model, Mathematics
Abstract: As recent advancements in AI and natural language processing have made it possible for language models to understand and generate human-like text, the field of mathematical language processing remains uniquely challenging. Mathematical texts often involve intricate symbolic notations, specialized terminology, and formal structures, necessitating tailored approaches for training models to handle such content effectively. Addressing these challenges, MathLLaMA leverages the LLaMA-Factory framework to create a model optimized for a variety of mathematical tasks, ranging from algebraic manipulation and calculus problem-solving to higher-level areas like discrete mathematics and number theory. This paper introduces MathLLaMA, a fine-tuned version of the LLaMA model, designed explicitly for mathematical problem-solving and reasoning. MathLLaMA leverages the LLaMA-Factory framework, which provides a comprehensive toolkit for training and fine-tuning LLMs. The primary objective of MathLLaMA is to extend the capabilities of LLaMA for use in mathematical domains by equipping it with the ability to understand formal mathematical language, reason through multi-step solutions, and generate accurate mathematical expressions. Our approach involves fine-tuning the model on a diverse set of mathematical datasets and using specialized techniques to address the unique challenges posed by mathematical texts.
Submission Number: 3
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