Summary of Finben: a Holistic Financial Benchmark For Large Language Models, by Qianqian Xie et al.

FinBen: A Holistic Financial Benchmark for Large Language Models

by 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

First submitted to arxiv on: 20 Feb 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary This paper introduces FinBen, a comprehensive open-source evaluation benchmark for language models in finance. The authors highlight the lack of standard evaluation methods and datasets for financial tasks, which hinders the development of language models (LLMs) in this field. To address this gap, they create FinBen, a set of 36 datasets covering seven critical aspects of finance, including information extraction, textual analysis, question answering, text generation, risk management, forecasting, and decision-making. The authors evaluate 15 representative LLMs on FinBen, revealing strengths and weaknesses in various tasks. They also introduce novel evaluation methods for agent and Retrieval-Augmented Generation (RAG) tasks. Their results demonstrate the potential of FinBen to drive innovation in financial LLMs.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper creates a special set of tools called FinBen that helps test how well computer programs can understand and work with money-related information. The authors think that these programs, called language models, are very good at some things like recognizing what’s written, but they’re not so good at other things like making smart decisions about money. They make FinBen by gathering lots of different examples of financial tasks, like looking for important information or predicting how the stock market will do. Then, they test 15 different language models on these tasks and find that some are really good at some things but not others. This helps us understand what language models can and can’t do, and it might even help make them better at helping us with money-related tasks.