Numerical Reasoning for Financial Reports
Abhinav Arun Ashish Dhiman Mehul Soni Yibei Hu
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.