Comparative Study of LLMs for Personal Financial Decision in Low Resource Language

Rashidat Damilola Sikiru, Olubayo Adekanmbi, Anthony Soronnadi

Keywords: LLMs, Low-Resource Language, Finance

Abstract:

Large language models have seen rapid progress in recent times, and this has resulted in many applications in diverse fields. The ability of LLMs to make use of large-scale text makes it relevant in the financial industry and for financial tasks. With the increasing availability of LLM models, these tasks can be considered easy or may be of use for a person who needs to track their financial lifestyle before visiting financial institutions. This study seeks to investigate the accuracy of LLM models in responding to basic day-to-day financial questions both in the
English and Yoruba languages. The result shows that ChatGPT4.0 outperformed ChatGPT3.5 and Bard(LaMDA) in all three phases. The result shows that these language models can be improved to fit in low-resource languages.

Submission Number: 25