Open-FinLLMs: Open Multimodal Large Language Models for Financial
  Applications
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.
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Qianqian Xie
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Xie
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QianqianXie1994
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Dong Li
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Mengxi Xiao
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Mengxi Xiao
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ElsaShaw
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Zihao Jiang
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Ruoyu Xiang
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Xiao Zhang
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Zhengyu Chen
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Yueru He
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Yueru1
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Weiguang Han
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Yuzhe Yang
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Yuzhe Yang
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TobyYang7
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Shunian Chen
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Shunian Chen
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Shunian
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Yifei Zhang
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Lihang Shen
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Daniel Kim
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kimd24
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Zhiwei Liu
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Yangyang Yu
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Yupeng Cao
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YupengCao
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Zhiyang Deng
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Zhiyuan Yao
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Haohang Li
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Duanyu Feng
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Yongfu Dai
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VijayaSai Somasundaram
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Peng Lu
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Yilun Zhao
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Yitao Long
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Kaleb Smith
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Min Peng
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Jordan W. Suchow
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Benyou Wang
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Wang
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Alejandro Lopez-Lira
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Jimin Huang
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Jimin Huang
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jiminHuang
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Sophia Ananiadou
Open-FinLLMs: Open Multimodal Large Language Models for Financial
  Applications
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.
Open-FinLLMs, a series of financial LLMs, enhance performance through comprehensive pre-training and instruction fine-tuning, demonstrating superior results in both zero-shot and few-shot evaluations, and excels in understanding tables and charts.
LLMs FinLLaMA pre-trained financial corpus instruction fine-tuned FinLLaMA-instruct FinLLaVA multimodal LLM image-text instructions zero-shot few-shot Sharpe Ratios trading simulations