This study seeks to test whether low-cost inference and efficient 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.

The use of small language models (SLMs), herein defined as models with less than three billion parameters, is increasing across various domains and applications. Due to their ability to run on more accessible hardware and preserve user privacy, SLMs possess the potential to democratize access to language models for individuals of different socioeconomic status and with different privacy preferences. This study assesses several state-of-the-art SLMs (e.g., Apple’s OpenELM, Microsoft’s Phi, Google’s Gemma, and the Tinyllama project) for use in the financial domain to support the development of financial literacy LMs. Democratizing access to quality financial information for those who are financially under educated is greatly needed in society, particularly as new financial markets and products emerge and participation in financial markets increases due to ease of access. We are the first to examine the use of open-source SLMs to democratize access to financial question answering capabilities for individuals and students. To this end, we provide an analysis of the memory usage, inference time, similarity comparisons to ground-truth answers, and output readability of prominent SLMs to determine which models are most accessible and capable of supporting access to financial information. We analyze zero-shot and few-shot learning variants of the models. The results suggest that some off-the-shelf SLMs merit further exploration and fine-tuning to prepare them for individual use, while others may have limits to their democratization. Code to replicate our experiments is shared.