Open-FinLLMs：面向金融应用的开放式多模态大型语言模型

Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications

August 20, 2024

作者: Qianqian Xie, Dong Li, Mengxi Xiao, Zihao Jiang, Ruoyu Xiang, Xiao Zhang, Zhengyu Chen, Yueru He, Weiguang Han, Yuzhe Yang, Shunian Chen, Yifei Zhang, Lihang Shen, Daniel Kim, Zhiwei Liu, Zheheng Luo, Yangyang Yu, Yupeng Cao, Zhiyang Deng, Zhiyuan Yao, Haohang Li, Duanyu Feng, Yongfu Dai, VijayaSai Somasundaram, Peng Lu, Yilun Zhao, Yitao Long, Guojun Xiong, Kaleb Smith, Honghai Yu, Yanzhao Lai, Min Peng, Jianyun Nie, Jordan W. Suchow, Xiao-Yang Liu, Benyou Wang, Alejandro Lopez-Lira, Jimin Huang, Sophia Ananiadou

cs.AI

摘要

大型语言模型（LLMs）已经在金融应用方面取得了进展，但它们通常缺乏足够的金融知识，并且在涉及表格和时间序列数据等多模态输入的任务中表现不佳。为了解决这些限制，我们引入了Open-FinLLMs，一系列金融LLMs。我们首先介绍了FinLLaMA，它在一个包含520亿个标记的金融语料库上进行了预训练，结合了文本、表格和时间序列数据，以嵌入全面的金融知识。然后，我们对FinLLaMA进行了573K个金融指令的指导微调，得到了FinLLaMA-instruct，从而提高了任务性能。最后，我们提出了FinLLaVA，这是一个多模态LLM，通过1.43M个图像文本指令进行训练，以处理复杂的金融数据类型。广泛的评估显示，FinLLaMA在19个数据集和4个数据集上的零样本和少样本设置中，表现优于LLaMA3-8B、LLaMA3.1-8B和BloombergGPT。FinLLaMA-instruct在15个数据集上的表现优于GPT-4和其他金融LLMs。FinLLaVA在4个多模态任务中在理解表格和图表方面表现出色。此外，FinLLaMA在交易模拟中实现了令人印象深刻的夏普比率，突显了其强大的金融应用能力。我们将不断维护和改进我们的模型和基准，以支持学术界和行业中持续创新。

English

Large language models (LLMs) have advanced financial applications, yet they often lack sufficient financial knowledge and struggle with tasks involving multi-modal inputs like tables and time series data. To address these limitations, we introduce Open-FinLLMs, a series of Financial LLMs. We begin with FinLLaMA, pre-trained on a 52 billion token financial corpus, incorporating text, tables, and time-series data to embed comprehensive financial knowledge. FinLLaMA is then instruction fine-tuned with 573K financial instructions, resulting in FinLLaMA-instruct, which enhances task performance. Finally, we present FinLLaVA, a multimodal LLM trained with 1.43M image-text instructions to handle complex financial data types. Extensive evaluations demonstrate FinLLaMA's superior performance over LLaMA3-8B, LLaMA3.1-8B, and BloombergGPT in both zero-shot and few-shot settings across 19 and 4 datasets, respectively. FinLLaMA-instruct outperforms GPT-4 and other Financial LLMs on 15 datasets. FinLLaVA excels in understanding tables and charts across 4 multimodal tasks. Additionally, FinLLaMA achieves impressive Sharpe Ratios in trading simulations, highlighting its robust financial application capabilities. We will continually maintain and improve our models and benchmarks to support ongoing innovation in academia and industry.