Large Language Models in Finance (FinLLMs)

Large language models (LLMs) have demonstrated remarkable capabilities and have attracted significant attention across diverse domains, including financial services. Despite the extensive research into general-domain LLMs and their immense potential in finance, financial LLMs (FinLLMs) research remains limited. This survey provides a comprehensive overview of FinLLMs, including their history, techniques, downstream tasks associated with datasets, evaluations, and opportunities and challenges. Firstly, we present a chronological overview of general-domain language models (LMs) through to current FinLLMs, including the GPT-series, selected open-source LLMs, and financial LMs. Secondly, we compare five techniques used across eight financial LMs, including training methods, training data, and fine-tuning methods. Thirdly, we summarize the performance evaluations of six benchmark tasks and datasets and provide eight advanced financial NLP tasks and datasets for developing more sophisticated FinLLMs. Finally, we discuss the opportunities and the challenges facing FinLLMs, such as hallucination, privacy, and efficiency. To support AI research in finance, we compile a collection of accessible datasets and benchmarks on GitHub. (