title: Open-FinLLMs: Open Multimodal Large Language Models for Financial
    Applications
summary: 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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paper_title: Open-FinLLMs: Open Multimodal Large Language Models for Financial
    Applications
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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