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 …

In this paper, we present a novel approach for distilling math word problem solving
capabilities from large language models (LLMs) into smaller, more efficient student models …

While significant progress has been made in natural language processing (NLP), existing
methods exhibit limitations in effectively interpreting and processing diverse mathematical …

Large language models (LLMs) can perform a wide range of tasks by following natural
language instructions, without the necessity of task-specific fine-tuning. Unfortunately, the …

Despite the remarkable success of Transformer-based Large Language Models (LLMs)
across various domains, understanding and enhancing their mathematical capabilities …

There is increasing interest in distilling task-specific knowledge from large language models
(LLM) to smaller student models. Nonetheless, LLM distillation presents a dual challenge: 1) …

Large language models (LLMs) have demonstrated impressive capabilities in mathematical
problem solving, particularly in single turn question answering formats. However, real world …

There is a growing trend of teaching large language models (LLMs) to solve mathematical
problems through coding. Existing studies primarily focus on prompting powerful, closed …

Mathematical reasoning, a core aspect of human cognition, is vital across many domains,
from educational problem-solving to scientific advancements. As artificial general …

Large Language Models (LLMs) employing Chain-of-Thought (CoT) prompting have
broadened the scope for improving multi-step reasoning capabilities. Usually, answer …