Mathematical reasoning is a fundamental aspect of human intelligence and is applicable in
various fields, including science, engineering, finance, and everyday life. The development …

Large language models have been shown to achieve remarkable performance across a
variety of natural language tasks using few-shot learning, which drastically reduces the …

We explore how generating a chain of thought---a series of intermediate reasoning steps---
significantly improves the ability of large language models to perform complex reasoning. In …

Large language models (LLMs), such as GPT-4, have shown remarkable performance in
natural language processing (NLP) tasks, including challenging mathematical reasoning …

The increasing scale of large language models (LLMs) brings emergent abilities to various
complex tasks requiring reasoning, such as arithmetic and commonsense reasoning. It is …

This paper embarks on an exploration into the Large Language Model (LLM) datasets,
which play a crucial role in the remarkable advancements of LLMs. The datasets serve as …

Chain-of-thought prompting (CoT) advances the reasoning abilities of large language
models (LLMs) and achieves superior performance in arithmetic, commonsense, and …

Solving math word problems requires deductive reasoning over the quantities in the text.
Various recent research efforts mostly relied on sequence-to-sequence or sequence-to-tree …

Large-scale pre-trained language models (PLMs) bring new opportunities to challenging
problems, especially those that need high-level intelligence, such as the math word problem …

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 …