Financial reports offer critical insights into a company's operations, yet
their extensive length typically spanning 30 40 pages poses challenges for
swift decision making in dynamic markets. To address this, we leveraged
finetuned Large Language Models (LLMs) to distill key indicators and
operational metrics from these reports basis questions from the user. We
devised a method to locate critical data, and leverage the FinQA dataset to
fine-tune both Llama-2 7B and T5 models for customized question answering. We
achieved results comparable to baseline on the final numerical answer, a
competitive accuracy in numerical reasoning and calculation.

财务报告通过分析公司的运营情况提供重要见解，但通常长度过长，约 30 至 40 页，对于动态市场的快速决策提出了挑战。为解决这个问题，我们利用经过微调的大型语言模型（LLM）从用户提出的问题中提炼关键指标和运营指标。我们设计了一种定位关键数据的方法，并利用 FinQA 数据集对 Llama-2 7B 和 T5 模型进行微调，以进行定制化问答。在最终的数值回答上取得了与基准相当的结果，在数值推理和计算上具有竞争力的准确性。