FinDABench: Benchmarking Financial Data Analysis Ability of Large Language Models
This work addresses the problem of assessing LLM capabilities in financial data analysis for researchers and practitioners, but it is incremental as it focuses on benchmarking rather than novel model improvements.
The paper tackles the uncertain proficiency of Large Language Models (LLMs) in financial data analysis by introducing FinDABench, a benchmark that evaluates LLMs across foundational, reasoning, and technical skill dimensions, with results including the release of evaluation scripts to foster advancement in this domain.
Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks. However, their proficiency and reliability in the specialized domain of financial data analysis, particularly focusing on data-driven thinking, remain uncertain. To bridge this gap, we introduce \texttt{FinDABench}, a comprehensive benchmark designed to evaluate the financial data analysis capabilities of LLMs within this context. \texttt{FinDABench} assesses LLMs across three dimensions: 1) \textbf{Foundational Ability}, evaluating the models' ability to perform financial numerical calculation and corporate sentiment risk assessment; 2) \textbf{Reasoning Ability}, determining the models' ability to quickly comprehend textual information and analyze abnormal financial reports; and 3) \textbf{Technical Skill}, examining the models' use of technical knowledge to address real-world data analysis challenges involving analysis generation and charts visualization from multiple perspectives. We will release \texttt{FinDABench}, and the evaluation scripts at \url{https://github.com/cubenlp/BIBench}. \texttt{FinDABench} aims to provide a measure for in-depth analysis of LLM abilities and foster the advancement of LLMs in the field of financial data analysis.