Small Language Models for the Democratization of Financial Literacy: Challenges and Opportunities

Tagore Rao Kosireddy, Jeffrey David Wall, Evan Lucas

Keywords: Financial Literacy, Small Language Models

Abstract: This study seeks to test whether low-cost inference and efficient \emph{Small Language Models} (SLMs) fine-tuned on existing open-source question answering datasets are capable of creating financial literacy chat bots that can answer financial questions for those with limited financial knowledge. The use of SLMs is growing in popularity across many domains, but SLMs are not thoroughly explored in the finance sector. This study offers an exploration of challenges and opportunities that exist in the finance sector to utilize SLMs for open-source financial question answering applications. In particular, this study examines the outputs of several open-source SLMs fine-tuned on the open-source FinGPT FiQA\_QA financial question answering dataset. We fine-tuned two versions of each model, one with an instruction prompt and one without an instruction prompt and compared the model outputs with ground truth human responses from the dataset. Further qualitative rating and analysis are provided for model outputs and the dataset. The exploration highlighted challenges with available open data and the fine-tuned SLMs. Existing open data sets in the financial AI research community are not sufficient to produce high-quality outputs with SLMs. Successful fine-tuning of SLMs has occurred in other domains with high quality data sets. We thus issue a call for new and better open financial question answering datasets that could result in higher-quality small language models.

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Submission Number: 3