  The recent success of Large Language Models (LLMs) has garnered significant
attention in both academia and industry. Prior research on LLMs has primarily
focused on enhancing or leveraging their generalization capabilities in zero-
and few-shot settings. However, there has been limited investigation into
effectively fine-tuning LLMs for a specific natural language understanding task
in supervised settings. In this study, we conduct an experimental analysis by
fine-tuning LLMs for the task of Chinese short text matching. We explore
various factors that influence performance when fine-tuning LLMs, including
task modeling methods, prompt formats, and output formats.
