  Large Language models (LLMs) usually rely on extensive training datasets. In
the financial domain, creating numerical reasoning datasets that include a mix
of tables and long text often involves substantial manual annotation expenses.
To address the limited data resources and reduce the annotation cost, we
introduce FinLLMs, a method for generating financial question-answering data
based on common financial formulas using Large Language Models. First, we
compile a list of common financial formulas and construct a graph based on the
variables these formulas employ. We then augment the formula set by combining
those that share identical variables as new elements. Specifically, we explore
formulas obtained by manual annotation and merge those formulas with shared
variables by traversing the constructed graph. Finally, utilizing GPT-3.5, we
generate financial question-answering data that encompasses both tabular
information and long textual content, building on the collected formula set.
Our experiments demonstrate that synthetic data generated by FinLLMs
effectively enhances the performance of several large-scale numerical reasoning
models in the financial domain, outperforming two established benchmark
financial question-answering datasets.
