Abstract

Large language models (LLMs) have shown the potential of revolutionizing natural language processing in diverse domains, sparking great interest in finance. However, the finance domain presents unique challenges, including high temporal sensitivity, constant dynamism, and a low signal-to-noise ratio (SNR). While proprietary models like BloombergGPT have taken advantage of their unique data accumulation, such privileged access calls for an open-source alternative to democratize internet-scale financial data.