This paper delves into the pivotal role of prompt engineering in unleashing the capabilities
of Large Language Models (LLMs). Prompt engineering is the process of structuring input …

Mathematical reasoning is a challenging task for large language models (LLMs), while the
scaling relationship of it with respect to LLM capacity is under-explored. In this paper, we …

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

In this paper, we present an innovative process-oriented math process reward model called
Math-shepherd, which assigns a reward score to each step of math problem solutions. The …

Large language models have made significant progress in various language tasks, yet they
still struggle with complex mathematics. In this paper, we propose ToRA a series of Tool …

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 …

Modern large language models (LLMs), such as ChatGPT, exhibit a remarkable capacity for
role-playing, enabling them to embody not only human characters but also non-human …

While language models are powerful and versatile, they often fail to address highly complex
problems. This is because solving complex problems requires deliberate thinking, which has …

Recent progress in large language models (LLMs) like GPT-4 and PaLM-2 has brought
significant advancements in addressing math reasoning problems. In particular, OpenAI's …

In recent years, there has been remarkable progress in leveraging Language Models (LMs),
encompassing Pre-trained Language Models (PLMs) and Large-scale Language Models …