Financial reports offer critical insights into a company's operations, yet their extensive length typically spanning 30 40 pages poses challenges for swift decision making in dynamic markets. To address this, we leveraged finetuned Large Language Models (LLMs) to distill key indicators and operational metrics from these reports basis questions from the user. We devised a method to locate critical data, and leverage the FinQA dataset to fine-tune both Llama-2 7B and T5 models for customized question answering. We achieved results comparable to baseline on the final numerical answer, a competitive accuracy in numerical reasoning and calculation.
@article{arxiv.2312.14870,
title = {Numerical Reasoning for Financial Reports},
author = {Abhinav Arun and Ashish Dhiman and Mehul Soni and Yibei Hu},
journal= {arXiv preprint arXiv:2312.14870},
year = {2023}
}