The FinBen: An Holistic Financial Benchmark for Large Language Models
Qianqian Xie Weiguang Han Zhengyu Chen Ruoyu Xiang Xiao Zhang Yueru He Mengxi Xiao Dong Li Yongfu Dai Duanyu Feng Yijing Xu Haoqiang Kang Ziyan Kuang Chenhan Yuan Kailai Yang Zheheng Luo Tianlin Zhang Zhiwei Liu Guojun Xiong Zhiyang Deng Yuechen Jiang Zhiyuan Yao Haohang Li Yangyang Yu Gang Hu Jiajia Huang Xiao-Yang Liu Alejandro Lopez-Lira Benyou Wang Yanzhao Lai Hao Wang Min Peng Sophia Ananiadou Jimin Huang
LLMs have transformed NLP and shown promise in various fields, yet their
potential in finance is underexplored due to a lack of thorough evaluations and
the complexity of financial tasks. This along with the rapid development of
LLMs, highlights the urgent need for a systematic financial evaluation
benchmark for LLMs. In this paper, we introduce FinBen, the first comprehensive
open-sourced evaluation benchmark, specifically designed to thoroughly assess
the capabilities of LLMs in the financial domain. FinBen encompasses 35
datasets across 23 financial tasks, organized into three spectrums of
difficulty inspired by the Cattell-Horn-Carroll theory, to evaluate LLMs'
cognitive abilities in inductive reasoning, associative memory, quantitative
reasoning, crystallized intelligence, and more. Our evaluation of 15
representative LLMs, including GPT-4, ChatGPT, and the latest Gemini, reveals
insights into their strengths and limitations within the financial domain. The
findings indicate that GPT-4 leads in quantification, extraction, numerical
reasoning, and stock trading, while Gemini shines in generation and
forecasting; however, both struggle with complex extraction and forecasting,
showing a clear need for targeted enhancements. Instruction tuning boosts
simple task performance but falls short in improving complex reasoning and
forecasting abilities. FinBen seeks to continuously evaluate LLMs in finance,
fostering AI development with regular updates of tasks and models.