Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| author | Huang, Jimin Xiao, Mengxi Li, Dong Jiang, Zihao Yang, Yuzhe Zhang, Yifei Qian, Lingfei Wang, Yan Peng, Xueqing Ren, Yang Xiang, Ruoyu Chen, Zhengyu Zhang, Xiao He, Yueru Han, Weiguang Chen, Shunian Shen, Lihang Kim, Daniel Yu, Yangyang Cao, Yupeng Deng, Zhiyang Li, Haohang Feng, Duanyu Dai, Yongfu Somasundaram, VijayaSai Lu, Peng Xiong, Guojun Liu, Zhiwei Luo, Zheheng Yao, Zhiyuan Weng, Ruey-Ling Qiu, Meikang Smith, Kaleb E Yu, Honghai Lai, Yanzhao Peng, Min Nie, Jian-Yun Suchow, Jordan W. Liu, Xiao-Yang Wang, Benyou Lopez-Lira, Alejandro Xie, Qianqian Ananiadou, Sophia Tsujii, Junichi |
| author_facet | Huang, Jimin Xiao, Mengxi Li, Dong Jiang, Zihao Yang, Yuzhe Zhang, Yifei Qian, Lingfei Wang, Yan Peng, Xueqing Ren, Yang Xiang, Ruoyu Chen, Zhengyu Zhang, Xiao He, Yueru Han, Weiguang Chen, Shunian Shen, Lihang Kim, Daniel Yu, Yangyang Cao, Yupeng Deng, Zhiyang Li, Haohang Feng, Duanyu Dai, Yongfu Somasundaram, VijayaSai Lu, Peng Xiong, Guojun Liu, Zhiwei Luo, Zheheng Yao, Zhiyuan Weng, Ruey-Ling Qiu, Meikang Smith, Kaleb E Yu, Honghai Lai, Yanzhao Peng, Min Nie, Jian-Yun Suchow, Jordan W. Liu, Xiao-Yang Wang, Benyou Lopez-Lira, Alejandro Xie, Qianqian Ananiadou, Sophia Tsujii, Junichi |
| contents | Financial LLMs hold promise for advancing financial tasks and domain-specific applications. However, they are limited by scarce corpora, weak multimodal capabilities, and narrow evaluations, making them less suited for real-world application. To address this, we introduce \textit{Open-FinLLMs}, the first open-source multimodal financial LLMs designed to handle diverse tasks across text, tabular, time-series, and chart data, excelling in zero-shot, few-shot, and fine-tuning settings. The suite includes FinLLaMA, pre-trained on a comprehensive 52-billion-token corpus; FinLLaMA-Instruct, fine-tuned with 573K financial instructions; and FinLLaVA, enhanced with 1.43M multimodal tuning pairs for strong cross-modal reasoning. We comprehensively evaluate Open-FinLLMs across 14 financial tasks, 30 datasets, and 4 multimodal tasks in zero-shot, few-shot, and supervised fine-tuning settings, introducing two new multimodal evaluation datasets. Our results show that Open-FinLLMs outperforms afvanced financial and general LLMs such as GPT-4, across financial NLP, decision-making, and multi-modal tasks, highlighting their potential to tackle real-world challenges. To foster innovation and collaboration across academia and industry, we release all codes (https://anonymous.4open.science/r/PIXIU2-0D70/B1D7/LICENSE) and models under OSI-approved licenses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_11878 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications Huang, Jimin Xiao, Mengxi Li, Dong Jiang, Zihao Yang, Yuzhe Zhang, Yifei Qian, Lingfei Wang, Yan Peng, Xueqing Ren, Yang Xiang, Ruoyu Chen, Zhengyu Zhang, Xiao He, Yueru Han, Weiguang Chen, Shunian Shen, Lihang Kim, Daniel Yu, Yangyang Cao, Yupeng Deng, Zhiyang Li, Haohang Feng, Duanyu Dai, Yongfu Somasundaram, VijayaSai Lu, Peng Xiong, Guojun Liu, Zhiwei Luo, Zheheng Yao, Zhiyuan Weng, Ruey-Ling Qiu, Meikang Smith, Kaleb E Yu, Honghai Lai, Yanzhao Peng, Min Nie, Jian-Yun Suchow, Jordan W. Liu, Xiao-Yang Wang, Benyou Lopez-Lira, Alejandro Xie, Qianqian Ananiadou, Sophia Tsujii, Junichi Computation and Language Computational Engineering, Finance, and Science Computational Finance Financial LLMs hold promise for advancing financial tasks and domain-specific applications. However, they are limited by scarce corpora, weak multimodal capabilities, and narrow evaluations, making them less suited for real-world application. To address this, we introduce \textit{Open-FinLLMs}, the first open-source multimodal financial LLMs designed to handle diverse tasks across text, tabular, time-series, and chart data, excelling in zero-shot, few-shot, and fine-tuning settings. The suite includes FinLLaMA, pre-trained on a comprehensive 52-billion-token corpus; FinLLaMA-Instruct, fine-tuned with 573K financial instructions; and FinLLaVA, enhanced with 1.43M multimodal tuning pairs for strong cross-modal reasoning. We comprehensively evaluate Open-FinLLMs across 14 financial tasks, 30 datasets, and 4 multimodal tasks in zero-shot, few-shot, and supervised fine-tuning settings, introducing two new multimodal evaluation datasets. Our results show that Open-FinLLMs outperforms afvanced financial and general LLMs such as GPT-4, across financial NLP, decision-making, and multi-modal tasks, highlighting their potential to tackle real-world challenges. To foster innovation and collaboration across academia and industry, we release all codes (https://anonymous.4open.science/r/PIXIU2-0D70/B1D7/LICENSE) and models under OSI-approved licenses. |
| title | Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications |
| topic | Computation and Language Computational Engineering, Finance, and Science Computational Finance |
| url | https://arxiv.org/abs/2408.11878 |