Raul Salles de Padua¹, Imran Qureshi², and Mustafa U. Karakaplan³, ¹Stanford University, USA, ²University of Texas at Austin, USA, ³University of South Carolina, USA

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 [1] 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 promptengineering approach on GPT-3 achieves near SOTA accuracy without any fine-tuning.

Question Answering, GPT-3, Financial Question Answering, Large Language Models, Information Retrieval, BERT, RoBERTa, FinQA