Large language models (LLMs) have demonstrated great potential in the
financial domain. Thus, it becomes important to assess the performance of LLMs
in the financial tasks. In this work, we introduce CFBenchmark, to evaluate the
performance of LLMs for Chinese financial assistant. The basic version of
CFBenchmark is designed to evaluate the basic ability in Chinese financial text
processing from three aspects~(\emph{i.e.} recognition, classification, and
generation) including eight tasks, and includes financial texts ranging in
length from 50 to over 1,800 characters. We conduct experiments on several LLMs
available in the literature with CFBenchmark-Basic, and the experimental
results indicate that while some LLMs show outstanding performance in specific
tasks, overall, there is still significant room for improvement in basic tasks
of financial text processing with existing models. In the future, we plan to
explore the advanced version of CFBenchmark, aiming to further explore the
extensive capabilities of language models in more profound dimensions as a
financial assistant in Chinese. Our codes are released at
this https URL

通过 CFBenchmark 评估了大型语言模型在中文金融助手中的性能，发现现有模型在金融文本处理的基本任务中仍有显著改进空间。