Assessing LLMs' Mathematical Reasoning in Financial Document Question Answering

arXiv:2402.11194v1 Announce Type: new
Abstract: Large Language Models (LLMs), excel in natural language understanding, but their capability for complex mathematical reasoning with an amalgamation of structured tables and unstructured text is uncertain. This study explores LLMs' mathematical reasoning on four financial tabular question-answering datasets: TATQA, FinQA, ConvFinQA, and Multihiertt. Through extensive experiments with various models and prompting techniques, we assess how LLMs adapt to complex tables and mathematical tasks. We focus on sensitivity to table complexity and performance variations with …

abstract arxiv capability cs.cl datasets document excel financial language language models language understanding large language large language models llms mathematical reasoning natural natural language question question answering reasoning study tables tabular text through type uncertain understanding unstructured