Financial analysis is an important tool for evaluating company performance. Practitioners work to answer financial questions to make profitable investment decisions, and use advanced quantitative analyses to do so. As a result, Financial Question Answering (QA) is a question answering task that requires deep reasoning about numbers. Furthermore, it is unknown how well pre-trained language models can reason in the financial domain. The current state-of-the-art requires a retriever to collect relevant facts about the financial question from the text and a generator to produce a valid financial program and a final answer. However, recently large language models like GPT-3 have achieved state-of-the-art performance on wide variety of tasks with just a few shot examples. We run several experiments with GPT-3 and find that a separate retrieval model and logic engine continue to be essential components to achieving SOTA performance in this task, particularly due to the precise nature of financial questions and the complex information stored in financial documents. With this understanding, our refined prompt-engineering approach on GPT-3 achieves near SOTA accuracy without any fine-tuning.

翻译：金融分析是评估企业绩效的重要工具。从业者通过解答金融问题来制定盈利投资决策，并借助高级量化分析实现这一目标。因此，金融问答（Financial QA）是一项要求深度推理数字的问答任务。此外，当前尚不清楚预训练语言模型在金融领域推理能力如何。现有最先进方法需要检索器从文本中收集金融问题的相关事实，以及生成器来产生有效的金融程序和最终答案。然而，近期如GPT-3等大型语言模型，仅通过少量样本示例就在广泛任务中达到了最先进的性能。我们通过对GPT-3进行多项实验发现，由于金融问题的精确性与金融文档中存储的复杂信息，独立的检索模型和逻辑引擎仍然是该任务实现SOTA性能的关键组件。基于这一认识，我们采用优化后的提示工程方法，在无需微调的情况下使GPT-3达到了接近SOTA的准确率。