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

GPT-3 Models are Few-Shot Financial Reasoners